Exploration of Word Embedding Model to Improve Context-Aware Recommender Systems

Recommender systems aim to assist users by recommending items that may be of interest to them. Traditionally, these systems use only user and item information. Over time, new information is being used, such as contextual information, which has improved the accuracy of the generated recommendations. In this work, we propose a context-aware recommender method that extracts contextual information from textual reviews using a word embedding based model. In addition, we propose two ways of considering textual contexts in recommender systems, the "Context of Reviews" and the "Context of Items". We evaluated our proposal by using the Yelp dataset (RecSysChallenge 2013); three baselines; and four context-aware recommender systems. In general, our proposal seems to be superior to the three baselines, mainly considering the "Context of Items", and the results were promising, allowing some lines of future work.