Enhancing Top-N Recommendation Using Stacked Autoencoder in Context-Aware Recommender System

Context-aware recommender systems (CARS) are a vital module of many corporate, especially within the online commerce domain, where consumers are provided with recommendations about products potentially relevant for them. A traditional CARS, which utilizes deep learning models considers that user's preferences can be predicted by ratings, reviews, demographics, etc. However, the feedback given by the users is often conflicting when comparing the rating score and the sentiment behind the reviews. Therefore, a model that utilizes either ratings or reviews for predicting items for top-N recommendation may generate unsatisfactory recommendations in many cases. In order to address this problem, this paper proposes an effective context-specific sentiment based stacked autoencoder (CSSAE) to learn the concrete preference of the user by merging the rating and reviews for a context-specific item into a stacked autoencoder. Hence, the user's preferences are consistently predicted to enhance the Top-N recommendation quality, by adapting the recommended list to the exact context where an active user is operating. Experiments on four Amazon (5-core) datasets demonstrated that the proposed CSSAE model persistently outperforms state-of-the-art top-N recommendation methods on various effectiveness metrics.