Context-aware recommendations via sequential predictions
Yong Wei Zheng, Alisha Anna Jose · 2019
Recommender systems have been widely applied to produce recommendations tailored by user preferences. Context-aware recommender systems additionally take context information (such as time, location, weather, companion, etc) into consideration to generate better recommendations, due to the fact that user tastes may vary from contexts to contexts. In this paper, we propose a novel recommender for context-aware recommendations in which we estimate the user preferences by sequential predictions. Our experimental results based on multiple real-world data sets demonstrate that the proposed approach is able to outperform the state-of-the-art context-aware recommendation techniques.