A multi-criteria item-based collaborative filtering framework (original) (raw)

An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems

International Journal of Advanced Computer Science and Applications, 2016

Recommender Systems are used to mitigate the information overload problem in different domains by providing personalized recommendations for particular users based on their implicit and explicit preferences. However, Item-based Collaborative Filtering (CF) techniques, as the most popular techniques of recommender systems, suffer from sparsity and new item limitations which result in producing inaccurate recommendations. The use of items' semantic information besides the inclusion of multi-criteria ratings can successfully alleviate such problems and generate more accurate recommendations. This paper proposes an Item-based Multi-Criteria Collaborative Filtering algorithm that integrates the items' semantic information and multi-criteria ratings of items to lessen known limitations of the item-based CF techniques. According to the experimental results, the proposed algorithm prove to be very effective in terms of dealing with both of the sparsity and new item problems and therefore produce more accurate recommendations when compared to standard itembased CF techniques.

Multi-criteria collaborative recommender

Second International Conference on the Innovative Computing Technology (INTECH 2012), 2012

Collaborative filtering algorithm (CF) is a personalized recommendation algorithm that is the most widely used in e-commerce. CF still needs to be improved so that it can make adequate recommendations and solve the problems such as scalability, smoothing the rating estimation. In this paper, we provide an approach of an item based collaborative filtering using item clustering prediction and including a new enhanced correlation similarity. Firstly, we cluster the items in some groups. Then, in the process of collaborative filtering recommendation, we need to calculate the similarity between the targeted item and items in the selected center. For this aim, an enhanced similarity measure based on multi criteria is proposed instead of the similarity based just on ratings' items. The objective is to consider when we calculate similarity, the integration of item rating information, the background of the item and the time-weight as criteria of the item evaluation into a convex model. In so doing, the amelioration of the similarity between items performs the recommendation. This proposed CF algorithm is showing to reduce also the influence of the former evaluation of the item.

A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations

Intelligent Automation & Soft Computing

Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and global reputation concepts to expand the space of potential recommenders. This expansion will enhance the prediction accuracy and coverage of the proposed model when applied to sparse data situations. To show effectiveness of the proposed model, a set of experiments are conducted on two real-world multi-criteria datasets, Yahoo! Movies and TripAdvisor. The experimental results demonstrate the superiority of the proposed model compared to a number of existing collaborative filtering-based recommendation methods under a variety of evaluation metrics.

Item-based Collaborative Filtering Recommendation Algorithms

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative filtering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative filtering techniques. Itembased techniques first analyze the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users.

Accuracy improvements for multi-criteria recommender systems

Proceedings of the 13th ACM Conference on Electronic Commerce - EC '12, 2012

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.

A User-Based Multi-Criteria Recommendation Approach for Personalized Recommendations

Recommender systems are information filtering systems designed to resolve the problem of information overload by automatically recommending items of interest to particular users based on their profiles or preferences. The user-based Collaborative Filtering (CF) techniques are very popular techniques and have been widely adopted in recommender systems. Despite their popularity, they suffer from the sparsity and new user problems, especially when insufficient rating information is available. Such limitations resulting in reduced recommendation accuracy and coverage. The addition of multi-criteria ratings besides the use of users' trust information can effectively mitigate such problems and produce more personalized recommendations. Accordingly, this paper proposes a User-based Multi-Criteria Recommendation approach that integrates the multi-criteria ratings of items and users' trust information to alleviate the abovementioned problems of user-based CF techniques. Experimental results show the significance of the proposed approach when compared with the standard user-based CF techniques in respect to the improvement of recommendation accuracy and coverage when confronted with sparsity and new user problems.

Neighbor Selection and Weighting in User-Based Collaborative Filtering

ACM Transactions on the Web, 2014

User-based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. The selection and weighting of these neighbors characterize the different recommendation approaches. While standard strategies perform a neighbor selection based on user similarities, trust-aware recommendation algorithms rely on other aspects indicative of user trust and reliability. In this paper we restate the trust-aware recommendation problem, generalizing it in terms of performance prediction techniques, whose goal is to predict the performance of an information retrieval system in response to a particular query. We investigate how to adopt the above generalization to define a unified framework where we conduct an objective analysis of the effectiveness (predictive power) of neighbor scoring functions. The proposed framework enables discriminating whether recommendation performance improvements are caused by the used neighbor scoring functions or by the ways these functions are used in the recommendation computation. We evaluated our approach with several state-ofthe-art and novel neighbor scoring functions on three publicly available datasets. By empirically comparing four neighbor quality metrics and thirteen performance predictors, we found strong predictive power for some of the predictors with respect to certain metrics. This result was then validated by checking the final performance of recommendation strategies where predictors are used for selecting and/or weighting user neighbors. As a result, we have found that, by measuring the predictive power of neighbor performance predictors, we are able to anticipate which predictors are going to perform better in neighbor scoring powered versions of a user-based collaborative filtering algorithm.

A new user similarity model to improve the accuracy of collaborative filtering

Knowledge-Based Systems, 2014

Collaborative filtering has become one of the most used approaches to provide personalized services for users. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations for users. However, most approaches related to this approach are based on similarity algorithms, such as cosine, Pearson correlation coefficient, and mean squared difference. These methods are not much effective, especially in the cold user conditions. This paper presents a new user similarity model to improve the recommendation performance when only few ratings are available to calculate the similarities for each user. The model not only considers the local context information of user ratings, but also the global preference of user behavior. Experiments on three real data sets are implemented and compared with many state-of-the-art similarity measures. The results show the superiority of the new similarity model in recommended performance.

n Implementation of the User-based Collaborative Filtering Algorithm

The explosive growth and availability of data on the internet has caused information overload. Searching for a query is not easy in the sources of information available for the interest of an individual user. Collaborative filtering systems recommend items based upon opinions of people with similar tastes. Collaborative filtering overcomes some difficulties faced by traditional information filtering by eliminating the need for computers to understand the content of the items. Further, collaborative filtering can also recommend articles that are not similar in content to items rated in the past as long as like-minded users have rated the items. Collaborative filtering (CF) is one of the most frequently used techniques in personalized recommendation systems. But currently used CF techniques are based on item rating prediction. We proposed an improved personalized recommended CF algorithm. Hybrid recommender systems or content-boosted technologies are quickly produce high quality recommendations. We have explored content-boosted CF technique which analyzes the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different Memorybased CF and Model-based CF techniques. Finally, we experimentally evaluate our results and compare them. The testing results show that in most cases, the improved algorithm that we put forward can improve recommendation quality.

Improving the Prediction Accuracy of Multicriteria Collaborative Filtering by Combination Algorithms

International Journal of Advanced Computer Science and Applications, 2014

This study focuses on developing the multicriteria collaborative filtering algorithm for improving the prediction accuracy. The approaches applied were user-item multirating matrix decomposition, the measurement of user similarity using cosine formula and multidimensional distance, individual criteria weight calculation, and rating prediction for the overall criteria by a combination approach. Results of the study show variation in multicriteria collaborative filtering algorithm, which was used for improving the document recommender system, with the two following characteristics-first, the rating prediction for four individual criteria using collaborative filtering algorithm by a cosine-based user similarity and a multidimensional distancebased user similarity; second, the rating prediction for the overall criteria using combination algorithms. Based on the results of testing, it can be concluded that a variety of models developed for the multicriteria collaborative filtering systems had much better prediction accuracy than for the classic collaborative filtering, which was characterized by the increasingly smaller values of Mean Absolute Error. The best accuracy was achieved by the multicriteria collaborative filtering system with multidimensional distance-based similarity.