标题：Detecting the latent associations hidden in multi-source information for better group recommendation
作者：Feng, Shanshan; Zhang, Huaxiang; Wang, Lei; Liu, Li; Xu, Yuchang
作者机构：[Feng, Shanshan; Zhang, Huaxiang; Liu, Li] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.; [Wang, Lei] Shandong Un 更多
通讯作者：Feng, SS;Feng, Shanshan
通讯作者地址：[Feng, SS]Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.
关键词：Group recommendation; Random walk model; Latent associations detection
摘要：Nowadays, most recommendation approaches used to suggest appreciate items for individual users. However, due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group recommendation. Unfortunately, most existing approaches used in group recommender systems make recommendations through aggregating individual preferences or individual predictive results rather than comprehensively investigating the social features that govern user choices made within a group. As a result, such approaches often fail to detect many latent factors that could potentially improve the performance of the group recommender systems. Therefore, we propose a new approach, random walks based on a topic model (RTM), for group recommendations through combining an integrated probabilistic topic model - a User Topic Model (UTM) with the Random Walk with Restart (RWR) method. The goal of the work in this paper is better identify group preferences by comprehensively detecting the latent associations among group members, in order to alleviate the data sparsity problem and improve the performance of group recommender systems. The UTM provides a latent framework of users, groups, and items by exploiting both the users' preference profiles and the items' content information, which together can describe group interests and item features in a more complete manner. This latent framework is then combined with RWR to predict the preference degrees of groups to unrated items. In particular, we develop two different recommendation strategies based on the proposed approach, and design a special random walk path for each developed recommendation strategy to comprehensively detect various latent associations. Finally, we conduct experiments to evaluate our approach and compare it with other state-of-the-art approaches using the real-world CAMRa2011 dataset. The results demonstrate the advantage of our approach over comparative ones. (C) 2019 Published by Elsevier B.V.