Journal ArticleDOI
Merging trust in collaborative filtering to alleviate data sparsity and cold start
TLDR
Results demonstrate that this novel method to incorporate social trust information (i.e., trusted neighbors explicitly specified by users) in providing recommendations outperforms other counterparts both in terms of accuracy and coverage.Abstract:
Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. However, it suffers from several inherent issues such as data sparsity and cold start. To address these problems, we propose a novel method called ''Merge'' to incorporate social trust information (i.e., trusted neighbors explicitly specified by users) in providing recommendations. Specifically, ratings of a user's trusted neighbors are merged to complement and represent the preferences of the user and to find other users with similar preferences (i.e., similar users). In addition, the quality of merged ratings is measured by the confidence considering the number of ratings and the ratio of conflicts between positive and negative opinions. Further, the rating confidence is incorporated into the computation of user similarity. The prediction for a given item is generated by aggregating the ratings of similar users. Experimental results based on three real-world data sets demonstrate that our method outperforms other counterparts both in terms of accuracy and coverage.read more
Citations
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Journal ArticleDOI
A reliability-based recommendation method to improve trust-aware recommender systems
Parham Moradi,Sajad Ahmadian +1 more
TL;DR: The proposed Reliability-based Trust-aware Collaborative Filtering method provides a dynamic mechanism to construct trust network of the users based on the proposed reliability measure to improve the reliability and also the accuracy of the predictions.
Journal ArticleDOI
Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions
TL;DR: This work reviews the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the solutions.
Journal ArticleDOI
Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks
TL;DR: A novel preference learning algorithm is designed to learn a confidence for each uncertain examination record with the help of transaction records and is called adaptive Bayesian personalized ranking (ABPR), which has the merits of uncertainty reduction on examination records and accurate pairwise preference learning on implicit feedbacks.
Journal ArticleDOI
Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems
TL;DR: This work develops a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships, which can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation.
References
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Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Book
Six Degrees: The Science of a Connected Age
TL;DR: Duncan Watts explores the science of networks and its implications, ranging from the Dutch tulipmania of the 17th century to the success of Harry Potter, from the impact of September 11 on Manhattan to the brain of the sea-slug, and from the processes that lead to stockmarket crashes to the structure of the world wide web.
Proceedings ArticleDOI
A matrix factorization technique with trust propagation for recommendation in social networks
Mohsen Jamali,Martin Ester +1 more
TL;DR: A model-based approach for recommendation in social networks, employing matrix factorization techniques and incorporating the mechanism of trust propagation into the model demonstrates that modeling trust propagation leads to a substantial increase in recommendation accuracy, in particular for cold start users.