Collaborative Filtering Research Papers - Academia.edu (original) (raw)

Today it is almost impossible to retrieve information with a keyword search when the information is spread over several pages. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Web personalization is the one application of semantic web usage mining. In this report we have explored comparison of various collaborative filtering techniques. Those techniques are memory based, model based and hybrid collaborative filtering. Our study shows that the performance of hybrid collaborative filtering technique is better than memory based and model based collaborative filtering technique. We have introduced normalization step, which will improve accuracy of traditional collaborative filtering techniques.

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- Machine Learning, Data Mining, Web Mining, Web Personalization

Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer’s interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.

Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel contextaware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music.

This paper presents a frame work for hardware acceleration for post video processing system implemented on FPGA. The deblocking filter algorithms ported on SOC having Altera NIOS-II soft core processor.SOC designed with the help of SOPC builder .Custom instructions are chosen by identifying the most frequently used tasks in the algorithm and the instruction set of NIOS-II processor has been extended. Deblocking filter new instruction added to the processor that are implemented in hardware and interfaced to the NIOS-II processor. New instruction added to the processor to boost the performance of the deblocking filter algorithm. Use of custom instructions the implemented tasks have been accelerated by 5.88%. The benefit of the speed is obtained at the cost of very small hardware resources.

We present a generalization of frequent itemsets allowing the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifies error-tolerant frequent clusters of items in transactional data (customer-purchase data, web browsing data, text, etc.). This efficient algorithm exploits sparsity of the underlying data to find large groups of items that are correlated over database records (rows). The notion of transaction coverage allows us to extend the algorithm and view it as a fast clustering algorithm for discovering segments of similar transactions in binary sparse data. We evaluate the new algorithm on three real-world applications: clustering high-dimensional data, query selectivity estimation and collaborative filtering. Results show that we consistently uncover structure in large sparse databases that other more traditional clustering algorithms in data mining fail to find.

, namely BIC-aiNet, capable of clustering rows and columns of a data matrix simultaneously. The usefulness and performance of the methodology are reported in the literature. Now, the authors carry out more rigorous comparative experiments with BIC-aiNet and other techniques found in the literature, as well as evaluate the scalability of the algorithm in several datasets of different sizes. The results indicate that our proposal is able to provide useful recommendations for the users, outperforming other methodologies for CF.

- by Claudio Toledo
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- Population Genetics, Genetic Algorithms, Scheduling, Hybrid Intelligent Systems

In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.

- by Sean McNee
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- Metrics, Diversification, Collaborative Filtering, User satisfaction
- by Julita Vassileva
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- Social Computing, Social learning, Collaborative Filtering, ITS

Food recommendation system is one of the most interesting recommendation problems since it provides data for decision-making to users on selection of foods that meets individual preference of each user. Personalized recommender system has been used to recommend foods or menus to respond to requirements and restrictions of each user in a better way. This research study aimed to develop a personalized healthy food recommendation system based on collaborative filtering and knapsack method. Assessment results found that users were satisfied with the personalized healthy food recommendation system based on collaborative filtering and knapsack problem algorithm which included ability of operating system, screen design, and efficiency of operating system. The average satisfaction score overall was 4.20 implying that users had an excellent level of satisfaction.

In this work we demonstrate the usefulness of the application of Recommender Systems in the financial domain. Specifically we investigate a dataset, made available by a major European bank, containing the purchases of a large set of investment assets by 200k investors. We also present some preliminary results of the application of network analysis via statistical validation to identify clusters of investment assets.

- by daniele regoli
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- Finance, Computer Science, Networks, Collaborative Filtering

Collaborative Filtering is one of the most widely used approaches in recommendation systems which predicts user preferences by learning past user-item relationships. In recent years, item-oriented collaborative filtering methods came into prominence as they are more scalable compared to useroriented methods. Item-oriented methods discover itemitem relationships from the training data and use these relations to compute predictions. In this paper, we propose a novel item-oriented algorithm, Random Walk Recommender, that first infers transition probabilities between items based on their similarities and models finite length random walks on the item space to compute predictions. This method is especially useful when training data is less than plentiful, namely when typical similarity measures fail to capture actual relationships between items. Aside from the proposed prediction algorithm, the final transition probability matrix computed in one of the intermediate steps can be used as an item similarity matrix in typical item-oriented approaches. Thus, this paper suggests a method to enhance similarity matrices under sparse data as well. Experiments on Movie-Lens data show that Random Walk Recommender algorithm outperforms two other item-oriented methods in different sparsity levels while having the best performance difference in sparse datasets.

- by Mukkai Krishnamoorthy
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- Collaborative Filtering, Random Walk, Sparsity, Recommender System

Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some modelbased techniques are more robust than k-nn. Model abstraction can inhibit certain aspects of an attack, providing an algorithmic approach to minimizing attack effectiveness. In this paper, we examine the robustness of a recommendation algorithm based on the data mining technique of association rule mining. Our results show that the Apriori algorithm offers large improvement in stability and robustness compared to k-nearest neighbor and other model-based techniques we have studied. Furthermore, our results show that Apriori can achieve comparable recommendation accuracy to k-nn.

- by Robin Burke and +1
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- Data Mining, Collaborative Filtering, Association Rule Mining, Recommender System
- by Carl M Kadie
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- Collaborative Filtering, Algorithm Design, User preferences, Correlation coefficient

Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity logs and product items, accurate and efficient recommendation is a challenging computational task. This paper introduces a new soft hierarchical clustering algorithm-Fuzzy Hierarchical Co-clustering (FHCC) algorithm, and applies this algorithm to detect user-product joint groups from users' behavior data for collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of applications according to the accessibility of data sources by carefully adjust the weights of different data sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product co-clusters. The results show that our proposed approach provide more meaningful recommendation results, and outperforms existing item-based and user-based collaborative filtering recommendations in terms of accuracy and ranked position.

Abstract. This article discusses a potential application of radio frequency identification (RFID) and collaborative filtering for targeted advertising in grocery stores. Every day hundreds of items in grocery stores are marked down for promotional purposes. Whether these promotions are effective or not depends primarily on whether the customers are aware of them or not, and secondarily whether the customers are interested in the products or not. Currently, the companies are incapable of influencing the customers ’ decisionmaking process while they are shopping. However, the capabilities of RFID technology enable us to transfer the recommendation systems of e-commerce to grocery stores. In our model, using RFID technology, we get real time information about the products placed in the cart during the shopping process. Based on that information we inform the customer about those promotions in which the customer is likely to be interested in. The selection of the product advertised is a...

- by Esma Nur Cinicioglu
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- Computer Science, RFID, Bayesian Networks, Collaborative Filtering

Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating based and ranking based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and his or her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach.

- by Shuaiqiang Wang
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- Collaborative Filtering, Vector Space Model

Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation and SVD based methods provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings where the known preference information does not change with time. However, a number of practical scenarios require dynamic real-time collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel collaborative filtering approach based on a recently proposed weighted co-clustering algorithm [3] that involves simultaneous clustering of users and items. We design incremental and parallel versions of the co-clustering algorithm and use it to build an efficient real-time collaborative filtering framework. Empirical evaluation of our approach on large movie and book rating datasets demonstrates that it is possible to obtain an accuracy comparable to that of the correlation and matrix factorization based approaches at a much lower computational cost.

- by Thomas George
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- Collaborative Filtering, Recommender System, Real Time, Matrix factorization

User-generated texts such as reviews, comments or discussions are valuable indicators of users' preferences. Unlike previous works which focus on labeled data from user-contributed reviews, we focus here on user comments which are not accompanied by explicit rating labels. We investigate their utility for a one-class collaborative filtering task such as bookmarking, where only the user actions are given as ground truth. We propose a sentiment-aware nearest neighbor model (SANN) for multimedia recommendations over TED talks, which makes use of user comments. The model outperforms significantly, by more than 25% on unseen data, several competitive baselines.

- by Andrei Popescu-belis
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- Sentiment Analysis, Collaborative Filtering

Automated Collaborative Filtering (ACF) refers to a group of algorithms used in recommender systems, a research topic that has received considerable attention due to its e-commerce applications. However, existing techniques are rarely capable of dealing with imperfections in user-supplied ratings. When such imperfections (e.g., ambiguities) cannot be avoided, designers resort to simplifying assumptions that impair the system's performance and utility. We have developed a novel technique referred to as CoFiDS-Collaborative Filtering based on Dempster-Shafer belief-theoretic framework-that can represent a wide variety of data imperfections, propagate them throughout the decision-making process without the need to make simplifying assumptions, and exploit contextual information. With its DS-theoretic predictions, the domain expert can either obtain a "hard" decision or can narrow the set of possible predictions to a smaller set. With its capability to handle data imperfections, CoFiDS widens the applicability of ACF to such critical and sensitive domains as medical decision support systems and defense-related applications. We describe the theoretical foundation of the system and report experiments with a benchmark movie data set. We explore some essential aspects of CoFiDS' behavior and show that its performance compares favorably with other ACF systems.

- by Kamal Premaratne and +1
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- Decision support system, Collaborative Filtering, Belief, Incomplete Information

This paper develops a music recommendation system that automates the downloading of songs into a mobile digital audio device. The system tailors the compositions of the songs to the preferences of individuals based on past behaviors. We describe and predict individual listening behaviors using a lognormal hazard function. Our recommendation system is the first to accomplish this and there is as of this moment no existing alternative. Our proposed approach provides an improvement over alternative methods that could be used for product recommendations. Our system has a number of distinct features. First, we use a Sequential Monte Carlo algorithm that enables the system to deal with massive historical datasets containing listening behavior of individuals. Second, we apply a variable selection procedure that helps to reduce the dimensionality of the problem, because in many applications the collection of songs needs to be described by a very large number of explanatory variables. Third, our system recommends a batch of products rather than a single product, taking into account the predicted utility and the uncertainty in the parameter estimates, and applying experimental design methods.

- by Roland Rust
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- Marketing, Information Technology, Personalization, Marketing (Library Science)

Factor analysis is a general purpose technique for dimension- ality reduction with applications in diverse areas including computer vision, collaborative filtering and computational bi- ology. Sparse factor analysis is a natural extension that can be motivated by the observation that sparse features tend to generalize better, or justified based on a priori beliefs about the underlying generative model of the

- by Oliver Stegle
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- Computer Vision, System Biology, Information Processing, Factor analysis

Recently, recommender system has an important role in e-commerce to market products for users. One of recommender system approach that used in e-commerce is Collaborative Filtering. This system works by providing product recommendations based on products liked by other users who have similar preferences. However, sparse conditions in user data will cause sparsity problems, namely the system is difficult to provide recommendations because of the lack of important information needed. Therefore, we propose an e-commerce product recommendation system based on Collaborative Filtering using Principal Component Analysis (PCA) and K-Means Clustering. K-Means is used to overcome sparsity problems and to form user clusters to reduce the amount of data that needs to be processed. While PCA is used to reduce data dimensions and improve clustering performance of K-Means. The test results using the sports product dataset on the Olist e-commerce show that the proposed system has a lower RMSE value...

Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from http://www.newwisdom.net demonstrate the effectiveness of the proposed approach.

- by Mitsunori Ogihara
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- Engineering, Graph Theory, Audio Signal Processing, Content Analysis

Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of information that lead following the information flow in real world be impossible. Recommender systems, as the most successful application of information filtering, help users to find items of their interest from huge datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems focus on trustworthy value on relation among users to make more reliable and accurate recommends. In this paper our focus is on trust based approach and discuss about the process of making recommendation in these method. Furthermore, we review different proposed trust metrics, as the most important step in this process.

- by Ben Schafer and +1
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- Information Systems, Information Retrieval, Information Overload, Collaborative Filtering

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products , are recommended rarely or not at all. However, recommending the ignored products in the "long tail" is critical for businesses as they are less likely to be discovered. Popularity bias is also against social justice where the entities need to have a fair chance of being served and represented. In this work, I investigate the problem of popularity bias in recommender systems and develop algorithms to address this problem.

- by Himan Abdollahpouri
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- Recommender Systems, Collaborative Filtering

In the area of ambient intelligence there is a need to address user needs according with context features. Recently, the synergy between context aware computing and collaborative filtering is leading to enhance recommender systems with capabilities always nearer to user needs. Specifically, in the domain of tourism it is useful to proactively suggest right sets of attractive locations, events and so on. This work defines a context aware recommender system aimed at suggesting pertinent points of interest (POIs) to tourists. In particular, the approach is strongly based on the synergy between soft computing and data mining techniques. The general framework integrates user profiles, history of social networking and POIs data. Then by defining collaborative filtering approach on the history meaningful POIs are extracted. Indeed, soft computing techniques are mainly applied in order to support activity of unsupervised users and POIs classification. On the other hand, data mining techniques are exploited in order to extract rules able to associate user profile and context features with an eligible set of recommendable POIs. Experimental results show performance in terms of recommendations accuracy.

- by enrico fischetti
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- Data Mining, Social Networking, Ubiquitous Computing, Soft Computing

Tourism becomes a common thing in an area. People usually spend their spare time for a vacation to a tourist place. As time passes, there are several new tourism spots that cause information about the tourism spots need to be well managed so that users can find information about the place of tourism in detail and practical. The above points encourage the author to discuss research on the design of Tourism Information Systems Information in East Java Province in Android Based. This information system is designed with waterfall method and built using JavaScript Web Object Notation (JSON) web service that can provide information about tourism place in East Java Province in detail and by utilizing tracking technology of Global Positioning System (GPS) from the Google Maps API that can help tourists to know the route to the tourism spots from the GPS point. The result is that this system can search tourist information in East Java Province in detail and show the route to the tourism place from the point of GPS, as well as the recommendation of the best tourism spot to other tourists which is seen by the rating of tourism spots.

- by Bayu Adi Styawan
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- Information Systems, Tourism Management, Web Services, Mobile Programming

Recommender systems (RS) have shown to be valuable tools on e-commerce sites which help the customers identify the most relevant items within large product catalogs. In systems that rely on collaborative filtering, the generation of the product recommendations is based on ratings provided by the user community. While in many domains users are only allowed to attach an overall rating to the items, increasingly more online platforms allow their customers to evaluate the available items along different dimensions. Previous work has shown that these criteria ratings contain valuable information that can be exploited in the recommendation process.

- by Fatih Gedikli and +1
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- Machine Learning, Feature Selection, Collaborative Filtering, Support Vector Regression

supporting the development and deployment of mobile services.

- by Mudar Rais
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- Mobile Computing, Displays, Collaborative Filtering, User interfaces

This paper describes the Information Technology Alignment Planning Process-a strategic IT planning process created to complement the corporate planning model used by a major Utility company in the Midwest. Corporate planning activities produced the divisional strategies, critical success factors, and goals that then were used to by the IT Alignment Planning process to align IT within the company.

- by Daniel Peak
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- Information Systems, Marketing, Information Technology, Information Management

With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques -collaborative filtering. Apart from giving an overview of collaborative filtering approaches, we present the experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in the collaborative filtering framework using datasets with different properties. While the k-Nearest Neighbor algorithm is usually used for collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Since collaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm (such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the other hand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We conclude that the quality of collaborative filtering recommendations is highly dependent on the sparsity of available data. Furthermore, we show that kNN is dominant on datasets with relatively low sparsity while SVMbased approaches may perform better on highly sparse data.

To develop a recommender system, the collaborative filtering is the best known approach, which considers the ratings of users who have similar rating profiles or rating patterns. Consistently, it is able to compute the similarity of users when there are enough ratings expressed by users. Therefore, a major challenge of the collaborative filtering approach can be how to make recommendations for a new user, that is called cold-start user problem. To solve this problem, there have been proposed a few efficient methods based on ask-to-rate technique in which the profile of a new user is made by integrating information gained from a quick interview. This paper is a review of these proposed methods and how to use the ask-to-rate technique. Consequently, they are categorized into non-adaptive and adaptive methods. Then, each category is analyzed and their methods are compared.

- by Možde Bahadorpour and +1
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- Recommender Systems, Collaborative Filtering

Recommender systems in TV applications mostly focusing on the recommendation of video-on-demand (VOD) content, though the major part of users' content consumption is realized on linear channel programs, termed EPG content. In this case study we present how we tackled the EPG recommendation task, which exhibits several differences compared to the VOD scenario, including the lack of explicit user feedbacks, the magnitude of cold start problem, as well as data cleaning and feature selection necessary to be applied on raw consumption data. We provide both offline and online model validation. First we showcase the typical approach in machine learning by evaluating models against recall in an offline setting. Then, we investigate in depth the real-world results of the recommendation app using the pretrained models, and analyze how personalized recommendation influence users watching behavior. The experimentation results are based on our recommender system deployed at a Canadian IPTV service provider using Microsoft Mediaroom middleware.

- by Dávid Zibriczky and +1
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- Data Mining, Recommender Systems, Interactive Media, IPTV

We describe a personalized recommender system designed to suggest new products to supermarket shoppers. The recommender functions in a pervasive computing environment, namely, a remote shopping system in which supermarket customers use Personal Digital Assistants (PDAs) to compose and transmit their orders to the store, which assembles them for subsequent pickup. The recommender is meant to provide an alternative source of new ideas for customers who now visit the store less frequently. Recommendations are generated by matching products to customers based on the expected appeal of the product and the previous spending of the customer. Associations mining in the product domain is used to determine relationships among product classes for use in characterizing the appeal of individual products. Clustering in the customer domain is used to identify groups of shoppers with similar spending histories. Cluster-specific lists of popular products are then used as input to the matching process.

- by RahulWhite Orange
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- Information Systems, Data Mining, Personalization, Pervasive Computing

The present work overviews the application of recom-mender systems in various financial domains. The relevant literature is investigated based on two directions. First, a domain-based cate-gorization is discussed focusing on those recommendation problems, where the existing literature is significant. Second, the application of various recommendation algorithms and data mining techniques is summarized. The purpose of this paper is providing a basis for further scientific research and product development in this field.

- by Dávid Zibriczky
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- Real Estate, Finance, Recommender Systems, Mutual Funds

This paper presents a metric to measure similarity between users, which is applicable in collaborative filtering processes carried out in recommender systems. The proposed metric is formulated via a simple linear combination of values and weights. Values are calculated for each pair of users between which the similarity is obtained, whilst weights are only calculated once, making use of a

- by Fernando Astudillo Ortega
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- Genetic Algorithms, Metrics, Performance, Genetic Algorithm

The main strengths of collaborative filtering (CF), the most successful and widely used filtering technique for recommender systems, are its cross-genre or 'outside the box' recommendation ability and that it is completely independent of any machine-readable representation of the items being recommended. However, CF suffers from sparsity, scalability, and loss of neighbor transitivity. CF techniques are either memory-based or model-based. While the former is more accurate, its scalability compared to model-based is poor. An important contribution of this paper is a hybrid fuzzy-genetic approach to recommender systems that retains the accuracy of memory-based CF and the scalability of model-based CF. Using hybrid features, a novel user model is built that helped in achieving significant reduction in system complexity, sparsity, and made the neighbor transitivity relationship hold. The user model is employed to find a set of likeminded users within which a memory-based search is carried out. This set is much smaller than the entire set, thus improving system's scalability. Besides our proposed approaches are scalable and compact in size, computational results reveal that they outperform the classical approach.

- by Kamal Bharadwaj
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- Genetics, Fuzzy Sets, Web Personalization, Collaborative Filtering

Recommendation Systems are omnipresent on the web nowadays. Most websites today are striving to provide quality recommendations to their customers in order to increase and retain their customers. In this paper, we present our approaches to style employment recommendation system for a career based social networking websites. We take a bottom-up approach: we start with deeply understanding and exploring the info and gradually build the smaller bits of the system. We also consider traditional approaches of advice systems like collaborative filtering and discuss its performance. Our experiments show the efficacy of our approaches.

- by IJRASET Publication
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- Collaborative Filtering, Recommendation Systems

Recommender system is a helpful tool for helping the user in cutting the time needs to find personalized products, documents, friends, places and services. In addition, the recommender system handles the century web problem: information overload. In the same time, many environments or technologies (i.e. cloud, mobile, social network) become popular today and facing the problem of large amount of information. Therefore, the researchers recognize that the recommender system is a suitable solution to this problem in those environments. This paper, reviews the recent research papers that were applied the recommender system in mobile, social network, or cloud environment. We classify these recommender systems into four groups (i.e. mobile recommender system, social recommender system, cloud recommender system and traditional (PC) recommender system) depending on technology or environment that the RS is applied in. This survey presents some compression, advantages and challenges of these types of recommender systems. Also, it will directly support researchers and professionals in their understanding of those types of recommender systems.

- by Bushra Alhijawi
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- Social Networks, Social Networking, Recommender Systems, Mobile Computing

Collaborative Filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems in recent years. However, most existing CF based recommender systems worked in a centralized way and suffered from its shortage in scalability as their calculation complexity increased quickly both in time and space when the record in user database increases. In this article, we first propose a distributed CF algorithm called PipeCF together with two novel approaches: significance refinement and unanimous amplification, to further improve the scalability and prediction accuracy. We then show how to implement this algorithm on a Peer-to-Peer (P2P) structure through distributed hash table method, which is the most popular and efficient P2P routing algorithm, to construct a scalable distributed recommender system. The experimental data show that the distributed CF-based recommender system has much better scalability than traditional centralized ones with comparable prediction efficiency and accuracy. q

A recommender system is an application to search and recommend items by predicting ratings based on the similarity of user's characteristic information. Item-Based Clustering Hybrid Method (ICHM) is one of hybrid recommender system that combines the collaborative filtering and content based filtering. The purpose of combination between content based filtering and collaborative filtering in ICHM is to overcome each filtering method shortcomings. ICHM recommender system has another advantage that it can predict new items that have no rating at all. This paper explains about the implementation and result analysis of ICHM applied on film recommendation data of MovieLens.org. The implementation is done based on literature study performed. The analysis is done by comparing Means Absolute Error (MAE) under some testing scenario. The analysis is carried out for two types of cases, which are cold start problem and non-cold start problem. The higher the number of clusters, the lower the MAE would be. c coefficient only affects MAE on non-cold start problem.

- by Missy Desmala
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- Collaborative Filtering, Recommender System

The virtual world overflowing with the digital items which make the searching, choosing and shopping hard tasks for users. The recommender system is a smart filtering tool for generate a list of potential favorite items for the user to reduce the time needed by user to
choose among a huge number of choices in websites and facilitate the process.
In that context, this thesis presents a novel technique that combines the ideas of item-based semantic similarity, n-criteria and multi-filtering criteria with the genetic-based recommender system. The genetic algorithm is utilized in order to predict the best list of items to the active user. Consequently, each individual in the population represents a candidate recommendation list. Each list subjects to three tests to measure the quality of it.
The proposed system alleviates the effect of the sparsity and cold start problems and makes the recommender system capable of generating recommendation without the need of using a similarity metric or requires any additional information provided by the hybrid system. Furthermore and due to the fact that there are many environments facing the information overload problem, the author presents a new classification of the recommender system based on the environment that is applied in.
The proposed system is evaluated against the state-of-the-art genetic-based recommender system and the traditional techniques that used in collaborative filtering recommender system. The results obtained show that the proposed method outperforms these algorithms in prediction accuracy by 24.3%, recommendation quality by 33.5% and performance (CPU time) by 45.4%. Moreover, the results showed that 69.5% of the recommended items are truly favorite items to the active user. The remainders 30.5% of the recommended items are potential favorite items.

- by Bushra Alhijawi
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- Recommender Systems, Genetic Algorithms, Semantic similarity, Genetic Algorithm

Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f(x) = x + b, which precompute the average difference between the ratings of one item and another for users who rated both. Slope one algorithms are easy to implement, efficient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates for real-world systems. The basic slope one scheme is suggested as a new reference scheme for collaborative filtering. By factoring in items that a user liked separately from items that a user disliked, we achieve results competitive with slower memory-based schemes over the standard benchmark EachMovie and Movielens data sets while better fulfilling the desiderata of CF applications.

- by Anna Maclachlan
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- Machine Learning, Data Mining, Recommender Systems, Knowledge Discovery

Two flavors of the recommendation problem are the explicit and the implicit feedback settings. In the explicit feedback case, users rate items and the user-item preference relationship can be modelled on the basis of the ratings. In the harder but more common implicit feedback case, the system has to infer user preferences from indirect information: presence or absence of events, such as a user viewed an item. One approach for handling implicit feedback is to minimize a ranking objective function instead of the conventional prediction mean squared error. The naive minimization of a ranking objective function is typically expensive. This difficulty is usually overcome by a trade-off: sacrificing the accuracy to some extent for computational efficiency by sampling the objective function. In this paper, we present a computationally effective approach for the direct minimization of a ranking objective function, without sampling. We demonstrate by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feedback recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics.

- by Domonkos Tikk
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- Collaborative Filtering, Mean square error, Ranking, Objective function

In this paper we examine the use of a mathematical procedure, called Principal Component Analysis, in Recommender Systems. The resulting filtering algorithm applies PCA on user ratings and demographic data, aiming to improve various aspects of the recommendation process. After a brief introduction to PCA, we provide a discussion of the proposed PCA- Demog algorithm, along with possible ways of

We investigate the use of dimensionality reduction to improve performance for a new class of data analysis software called "recommender systems". Recommender systems apply knowledge discovery techniques to the problem of making product recommendations during a live customer interaction. These systems are achieving widespread success in E-commerce nowadays, especially with the advent of the Internet. The tremendous growth of customers and products poses three key challenges for recommender systems in the E-commerce domain. These are: producing high quality recommendations, performing many recommendations per second for millions of customers and products, and achieving high coverage in the face of data sparsity. One successful recommender system technology is collaborative filtering, which works by matching customer preferences to other customers in making recommendations. Collaborative filtering has been shown to produce high quality recommendations, but the performance degrades with the number of customers and products. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very largescale problems. This paper presents two different experiments where we have explored one technology called Singular Value Decomposition (SVD) to reduce the dimensionality of recommender system databases. Each experiment compares the quality of a recommender system using SVD with the quality of a recommender system using collaborative filtering. The first experiment compares the effectiveness of the two recommender systems at predicting consumer preferences based on a database of explicit ratings of products. The second experiment compares the effectiveness of the two recommender systems at producing Top-N lists based on a real-life customer purchase database from an E-Commerce site. Our experience suggests that SVD has the potential to meet many of the challenges of recommender systems, under certain conditions.