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 {\em social matrix factorization} (Social MF) and {\em
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 {\em \mbox{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.

本文以 user-generated content 为基础，提出了一种新的模型 MR3，将 collaborative filtering、social matrix factorization 和 topic matrix factorization 综合起来，加入了 implicit feedback 从而提高了其预测精度。

融合主题和社交潜在因素的协同过滤算法在隐式反馈中的应用

Collaborative Filtering with Topic and Social Latent Factors  Incorporating Implicit Feedback

Recommender systems (RSs) provide an effective way of alleviating the
information overload problem by selecting personalized choices. Online social
networks and user-generated content provide diverse sources for recommendation
beyond ratings, which present opportunities as well as challenges for
traditional RSs. Although social matrix factorization (Social MF) can integrate
ratings with social relations and topic matrix factorization can integrate
ratings with item reviews, both of them ignore some useful information. In this
paper, we investigate the effective data fusion by combining the two
approaches, in two steps. First, we extend Social MF to exploit the graph
structure of neighbors. Second, we propose a novel framework MR3 to jointly
model these three types of information effectively for rating prediction by
aligning latent factors and hidden topics. We achieve more accurate rating
prediction on two real-life datasets. Furthermore, we measure the contribution
of each data source to the proposed framework.

本文旨在探讨如何通过数据融合来提高评分预测的准确性，通过扩展 Social MF 模型以及提出新的 MR3 框架，即通过整合邻居关系、评分和隐藏主题等三种信息来创造新的推荐系统，提高了推荐系统的准确性。本文通过两个真实世界数据集的评测衡量了每种数据来源对所提出框架的贡献。