Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

03/26/2018
by   Guang-Neng Hu, et al.
0

Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized items for different users. Latent factors based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability. Recently, online social networks and user-generated content provide diverse sources for recommendation beyond ratings. Although social matrix factorization (Social MF) and topic matrix factorization (Topic MF) successfully exploit social relations and item reviews, respectively, both of them ignore some useful information. In this paper, we investigate the effective data fusion by combining the aforementioned approaches. First, we propose a novel model MR3 to jointly model three sources of information (i.e., ratings, item reviews, and social relations) effectively for rating prediction by aligning the latent factors and hidden topics. Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility. We achieve more accurate rating prediction on real-life datasets over various state-of-the-art methods. Furthermore, we measure the contribution from each of the three data sources and the impact of implicit feedback from ratings, followed by the sensitivity analysis of hyperparameters. Empirical studies demonstrate the effectiveness and efficacy of our proposed model and its extension.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/11/2016

A Synthetic Approach for Recommendation: Combining Ratings, Social Relations, and Reviews

Recommender systems (RSs) provide an effective way of alleviating the in...
research
10/29/2014

Latent Feature Based FM Model For Rating Prediction

Rating Prediction is a basic problem in Recommender System, and one of t...
research
08/08/2023

UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations

Recommender systems aim to enhance the overall user experience by provid...
research
09/08/2022

Tag-Aware Document Representation for Research Paper Recommendation

Finding online research papers relevant to one's interests is very chall...
research
04/05/2020

Designing and Connectivity Checking of Implicit Social Networks from the User-Item Rating Data

Implicit Social Network is a connected social structure among a group of...
research
03/30/2020

Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests

Collaborative Filtering is largely applied to personalize item recommend...
research
10/22/2018

Alternating Linear Bandits for Online Matrix-Factorization Recommendation

We consider the problem of online collaborative filtering in the online ...

Please sign up or login with your details

Forgot password? Click here to reset