Although Recommender Systems have been comprehensively studied in the past decade both in industry and academia, most of current recommender systems suffer from the following issues: 1) The data sparsity of the user-item matrix seriously affect the recommender system quality. As a result, most of traditional recommender system approaches are not able to deal with the users who have rated few items, which is known as cold start problem in recommender system. 2) Traditional recommender systems assume that users are independently and identically distributed and ignore the social relation between users. However, in real life scenario, due to the exponential growth of social networking service, such as facebook and Twitter, social connections between different users play an significant role for recommender system task. In this work, aiming at providing a better recommender systems by incorporating user social network information, we propose a matrix factorization framework with user social connection constraints. Experimental results on the real-life dataset shows that the proposed method performs significantly better than the state-of-the-art approaches in terms of MAE and RMSE, especially for the cold start users.
Although recommender systems have been widely used, such as Google, Amazon, Ebay, most of recommender systems suffer from several common drawbacks. First of all, data sparsity is the most serious problems for designing effective and efficient recommender systems. As indicated in @cite_16 @cite_22 @cite_17 , the density of data is less than @math 1 few items in amazon compared to the whole items in amazon database. So most of collaborative filtering based recommender systems such as @cite_0 @cite_19 @cite_23 @cite_1 @cite_20 @cite_9 @cite_24 @cite_5 @cite_13 cannot handle the users who have rated few items. What is more, traditional recommender systems ignore the social connection among users in both social based network and trust based network. It assumes that all the users are independently and identically distributed and only use the user-item matrix for recommendation task. However, in real life, we always turn to our trusted friend for suggestions for buying books, cloth, and watching movies, etc. So our preference can be easily affected by our friends. Therefore, simply ignoring social connection is not realistic when doing recommendation systems and considering the social connection information in recommender system can efficiently deal with the cold start users @cite_7 @cite_6 .
@cite_22 @cite_7 @cite_9 @cite_1 @cite_6 @cite_0 @cite_19 @cite_24 @cite_23 @cite_5 @cite_16 @cite_13 @cite_20 @cite_17
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In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the development of social network, trust measure introduced as a new approach to overcome the CF problems. On the other hand, trust-aware recommender systems are techniques to make use of trust statements and user personal data in social networks to improve the accuracy of rating prediction for cold start users. In addition, clustering-based recommender systems are other kind of systems that to be efficient and scalable to large-scale data sets but these systems suffer from relatively low accuracy and especially coverage too. Therefore to address these problems, in this paper we proposed a multi-view clustering based on Euclidean distance by combination both similarity view and trust relationships that is including explicit and implicit trusts. In order to analyze the effectiveness of the proposed method we used the real-world FilmTrust dataset. The experimental results on this data sets show that our approach can effectively improve both the accuracy and especially coverage of recommendations as well as in the cold start problem. Recommender systems are widely used in online e-commerce applications to improve user engagement and then to increase revenue. A key challenge for recommender systems is providing high quality recommendation to users in cold-start" situations. We consider three types of cold-start problems: 1) recommendation on existing items for new users; 2) recommendation on new items for existing users; 3) recommendation on new items for new users. We propose predictive feature-based regression models that leverage all available information of users and items, such as user demographic information and item content features, to tackle cold-start problems. The resulting algorithms scale efficiently as a linear function of the number of observations. We verify the usefulness of our approach in three cold-start settings on the MovieLens and EachMovie datasets, by comparing with five alternatives including random, most popular, segmented most popular, and two variations of Vibes affinity algorithm widely used at Yahoo! for recommendation. Recommender Systems based on Collaborative Filtering suggest to users items they might like, such as movies, songs, scientific papers, or jokes. Based on the ratings Based on the ratings provided by users about items, they first find users similar to the users receiving the recommendations and then suggest to her items appreciated in past by those like-minded users. However, given the ratable items are many and the ratings provided by each users only a tiny fraction, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network in order to find users that can be trusted by the active user. Items appreciated by these trustworthy users can then be recommended to the active user. An empirical evaluation on a large dataset crawled from Epinions.com shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings, so that trust is able to alleviate the cold start problem and other weaknesses that beset Collaborative Filtering Recommender Systems. 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 ltering 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 ltering 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 ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.