Recommendation Modeling Through Media Usage Behavior Topic Analysis

As online shopping and mobile media are recently activated, various feedbacks are built on users' preferences and purchases. Various approaches are being studied to improve the performance of recommendation considering individual characteristics. This study carries out clustering from media panel data through LDA topic modeling to imply the meaning of user's media use behavior, and applies a new rating, calculated by combining the existing rating and sentiment analysis on media use by cluster, to the recommendation system. We select households living with their children, and make a recommendation 31 smart devices that could be helpful for the members. We utilize the SAMC algorithm for exact inference of LDA topic modeling, and set the optimal number of topics through coherence and perplexity. We use BPR and collaborative filtering for the recommendation algorithm, and BPR provides the best performance by comparing with three performance indicators. It is indicated that the recommendation process suggested by this study provides a reasonable recommendation considering individual characteristics.