A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users

no code implementations • 20 Jun 2017 • Wang Hao, Fu Yanmei, Wang Qinyong, Yin Hongzhi, Du Changying, Xiong Hui

In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews.

Decision Making Recommendation Systems

Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation

no code implementations • 12 Mar 2015 • Wang Weiqing, Yin Hongzhi, Chen Ling, Sun Yizhou, Sadiq Shazia, Zhou Xiaofang

Geo-SAGE considers both user personal interests and the preference of the crowd in the target region, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items.

Recommendation Systems