Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local information is obtained from communities, and the global ones are based on the ratings. Here, a new fuzzy community detection using the personalized PageRank metaphor is introduced. The fuzzy membership values of the users to the communities are utilized to define a similarity measure. The method is evaluated by using two well-known datasets: MovieLens and FilmTrust. The results show that our method outperforms recent recommender systems.
Guo @cite_17 proposed three various approaches from the point of view of preference modeling to alleviate data sparsity and cold start problem. Low accuracy and coverage also are the issues of recommender system which have insufficient ratings. So this work addresses these issues in his proposed method, too. Firstly, it combines the ratings of trusted neighbors and makes a new rating profile for the active users. Secondly, it introduces a new Bayesian similarity measure in order to make better use of user ratings. Thirdly, it eliminates the concerned issues by proposing a new information source based on virtual product experience in virtual reality environments.
@cite_17
2070845054 2403905660 2117346966 2050096199
Our research aims to tackle the problems of data sparsity and cold start of traditional recommender systems. Insufficient ratings often result in poor quality of recommendations in terms of accuracy and coverage. To address these issues, we propose three different approaches from the perspective of preference modelling. Firstly, we propose to merge the ratings of trusted neighbors and thus form a new rating profile for the active users, based on which better recommendations can be generated. Secondly, we aim to make better use of user ratings and introduce a novel Bayesian similarity measure by taking into account both the direction and length of rating vectors. Thirdly, we propose a new information source called prior ratings based on virtual product experience in virtual reality environments, in order to inherently resolve the concerned problems. 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. Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different clusters simultaneously, we employ a support vector regression model to determine a prediction for a given item, based on user-, item- and prediction-related features. To accommodate (cold) users who cannot be clustered due to insufficient data, we propose a probabilistic method to derive a prediction from the views of both ratings and trust relationships. Experimental results on three real-world data sets demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation, moving clustering-based recommender systems closer towards practical use. Most existing recommender systems focus on modeling the ratings while ignoring the abundant information embedded in the review text. In this paper, we propose a unified model that combines content-based filtering with collaborative filtering, harnessing the information of both ratings and reviews. We apply topic modeling techniques on the review text and align the topics with rating dimensions to improve prediction accuracy. With the information embedded in the review text, we can alleviate the cold-start problem. Furthermore, our model is able to learn latent topics that are interpretable. With these interpretable topics, we can explore the prior knowledge on items or users and recommend completely "cold"' items. Empirical study on 27 classes of real-life datasets show that our proposed model lead to significant improvement compared with strong baseline methods, especially for datasets which are extremely sparse where rating-only methods cannot make accurate predictions.