Analyses of Collaborative Filtering Using Item Clustering and Hybrid Clustering (original) (raw)
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A Survey Paper on Clustering-based Collaborative Filtering Approach to Generate Recommendations
2015
The rapid development of information technology takes our shopping into the orbit of information. With the network construction of resources, the amount of shopping resources increases rapidly. Collaborative filtering (CF) is a technique commonly used to build personalized recommendations on the Web. Some popular websites that make use of the collaborative filtering technology include Amazon, Netflix, iTunes, IMDB. The most important issue which influences the collaborative filtering recommendation accuracy is the so-called data sparseness. Data sparseness causes the system difficulty in determining the nearest neighbors of the target user accurately. Clustering can solve this problem to some extent. Grouping a set of physical or abstract objects into classes of similar objects, this process is called as clustering. This paper presents the methods to generate recommendations using clustering-based collaborative filtering approach.
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Collaborative filtering is becoming greatly popular as it contributes in reducing information overload. Collaborative filtering based recommender system focuses on predicting new items of interest for a user based on correlations computed between that user and other users. In this paper we propose a framework based on, application of data partitioning/clustering algorithm on ratings dataset followed by collaborative filtering for developing a Movie Recommender System. The proposed system reduces the computation time considerably and increases the prediction accuracy.
With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, Amazon, etc. Hence, the collaborative filtering recommendation algorithms are highly valuable and play a vital role at the success of such businesses in reaching out to new users and promoting their services and products. With the aim of improving the recommendation performance of such an algorithm, this paper proposes a new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. The k-means algorithm and Singular Value Decomposition (SVD) are both used to cluster similar users and reduce the dimensionality. It proposes and evaluates an effective two stage recommender system that can generate accurate and highly efficient recommendations. The experimental results show that this new method significantly improves the performance of the recommendation systems .
A Survey on Collaborative Filtering Based Recommendation System
Recommender system (RS) is a revolutionary technique which has transformed the applications from content based to customer centric. It is the method of finding what the customer wants, it can either be data or an item. The ability to collect and compute information has enabled the emergence of recommendation techniques, and these techniques provides a better understanding of users and clients. The innovation behind recommender frameworks has advanced in the course of recent years into a rich accumulation of tools that induces the researcher and scientist to create precise recommenders. This article provides an outline of recommender systems and explains in detail about the collaborative filtering. It also defines various limitations of traditional recommendation methods and discusses the hybrid extensions by merging spatial properties of the user (item-based collaborative filtering) with users personalized preferences (user-based collaborative filtering). This hybrid system is applicable to a broader range of applications. It helps the user to find the items of their interest quickly and more precisely.
International Journal of Computational Science and Engineering, 2017
Clustering is one of the successful approaches of the model-based collaborative filtering techniques that deals with the problem of sparsity and provides quality recommendations. In the proposed work, fuzzy c-means clustering technique is adopted in order to produce item-based clusters as well as user-based clusters. Subsequently, collaborative filtering technique explores the item-based and user-based clusters and generates the list of item-based and user-based predictions, respectively. Further, to enhance the quality of recommendations, a novel weighted hybrid scheme is designed which integrates the user-based and item-based predictions to capture the influence of each active user towards item-based and user-based predictions. The proposed schemes are further categorised on the basis of re-clustering and without re-clustering under different similarity measures over sparse and dense datasets. The experimental results reveal that the variants of the proposed hybrid schemes consistently generate better results in comparison with the corresponding variants of proposed user-based schemes and the traditional item-based schemes.
Hybrid User-Item Based Collaborative Filtering
Procedia Computer Science, 2015
Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative filtering (CF) algorithms face two major challenges: data sparsity and scalability. In this study, we propose a hybrid method based on item based CF trying to achieve a more personalized product recommendation for a user while addressing some of these challenges. Case Based Reasoning (CBR) combined with average filling is used to handle the sparsity of data set, while Self-Organizing Map (SOM) optimized with Genetic Algorithm (GA) performs user clustering in large datasets to reduce the scope for item-based CF. The proposed method shows encouraging results when evaluated and compared with the traditional item based CF algorithm.
improvement of personalized recommendation algorithm based on hybrid collaborative filtering
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.
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The study aims to present an architecture for a recommendation system based on user items that are transformed into narrow categories. In particular, to identify the movies a user will likely watch based on their favorite items. The recommendation system focuses on the shortest connections between item correlations. The degree of attention paid to user-group relationships provides another valuable piece of information obtained by joining the sub-groups. Various relationships have been used to reduce the data sparsity problem. We reformulate the existing data into several groups of items and users. As part of the calculations and containment of activities, we consider Pearson similarity, cosine similarity, Euclidean distance, the Gaussian distribution rule, matrix factorization, EM algorithm, and k-nearest neighbors (KNN). It is also demonstrated that the proposed methods could moderate possible recommendations from diverse perspectives.
A new Recommendation Model for the User Clustering-Based Recommendation System
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The aim of this paper is to create a new recommendation method that would evaluate the peculiarities of user groups, and to examine experimentally the efficiency of user clustering in order to improve the recommendations. To achieve this goal, we have analysed recommendation systems (RS), their components, operating principles and data, used for accuracy evaluation. The proposed method is based on user clustering; therefore, clustering-based RS are reviewed. Finally, the proposed method is presented and tested with the most appropriate data set of all that discussed in the overview. The research has disclosed dependencies of the efficiency of recommendations on the number of clusters. The experimental results have shown that the proposed method can be applied to high density databases and the results of recommendations are better than those of traditional methods.
A Survey of Challenges in Collaborative Filtering Recommender System
Abstract— Recommender System predicts user preferences for the purpose of suggesting items to purchase or examine and Recommender systems using collaborative filtering are a popular technique for reducing information overload. Recommendation techniques that are having many classes but this paper focused on some filtering approaches like Content Based Filtering, Hybrid Recommendation and Collaborative Filtering approaches. Analysis, review of Recommender System, highlighting the architectural principles, key concepts, and state-of-the-art of implementation as well as the research challenges are the high lights of this paper. The important research directions and to provide better understanding of the design challenges of Collaborative Filtering are also focused in this paper.