Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation
نویسندگان
چکیده
منابع مشابه
Matrix Factorization with Explicit Trust and Distrust Relationships
With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their increasing popularity, in general recommender systems suffer from the data sparsity and the cold-start problems. To alleviate these issues, in re...
متن کاملHierarchical Bayesian Matrix Factorization with Side Information
Bayesian treatment of matrix factorization has been successfully applied to the problem of collaborative prediction, where unknown ratings are determined by the predictive distribution, inferring posterior distributions over user and item factor matrices that are used to approximate the user-item matrix as their product. In practice, however, Bayesian matrix factorization suffers from cold-star...
متن کاملCollaborative Topic Regression with Social Matrix Factorization for Recommendation Systems
Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popular platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user’s social circle in their decision process. In this paper, we are interested in examining the effectiveness of social network information to predict the user’s ratings...
متن کاملLeveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation
Trust has been used to replace or complement ratingbased similarity in recommender systems, to improve the accuracy of rating prediction. However, people trusting each other may not always share similar preferences. In this paper, we try to fill in this gap by decomposing the original single-aspect trust information into four general trust aspects, i.e. benevolence, integrity, competence, and p...
متن کاملContent-Based Social Recommendation with Poisson Matrix Factorization
We introduce Poisson Matrix Factorization with Content and Social trust information (PoissonMF-CS), a latent variable probabilistic model for recommender systems with the objective of jointly modeling social trust, item content and user’s preference using Poisson matrix factorization framework. This probabilistic model is equivalent to collectively factorizing a non-negative user–item interacti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ACM Transactions on Information Systems
سال: 2014
ISSN: 1046-8188,1558-2868
DOI: 10.1145/2641564