Deep Learning-Based Context-Aware Recommender System Considering Change in Preference

Abstract

In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods.

A Privacy-Preserving Time-Aware Method for Next POI Recommendation

“…The PPCM model first tried to discover similar users on different IoT platforms by locality-sensitive hashing in the time dimension and then considered group preferences (group similarity) to predict POI categories. Jeong and Kim [10] proposed a context-aware recommender method that considered the change in users' preferences over time. The proposed method divided data into time units to account for temporality and adopted a preference transition matrix to detect preference change.…”