A combination of temporal and general preferences for app recommendation

User preferences in various kinds of recommendations are in general made from the contents of recommending targets or the patterns that the targets are consumed in. As a result, a great number of previous works have focused on designing a good user preference. However, one important thing that is missed in the previous studies on user preference is that user preferences are affected by time. That is, it is of importance to capture the change of user preferences over time for better recommendations. This phenomenon is salient especially in using mobile apps. Therefore, this paper presents a time-based personalized application recommendation system which captures temporal changes in user preference. The proposed recommendation system can recommend dynamically the apps from an application market by considering the user preference and time. In order to recommend apps, the app descriptions are used to recommend new apps to users, and user preference is modeled using a probabilistic topic model from the descriptions. In order to incorporate time to the topic model, the proposed temporal topic model considers the usage of mobile apps over time for a specific user. The main problem of this temporal topic model is that it is not well trained when the number of apps that the user has used is small, and it can be remedied by incorporating a normal LDA-based topic model. As a result, the final recommendation model is a combination of temporal and LDA-based topic models. The proposed method is validated through a series of experiments. For app usages of three users for 35 days on average, it is compared with LDA-based topic model and the model that uses only temporal topic model. According to the experimental results, the proposed method outperforms the two baseline models up to 18% point in nDCG. This result proves that the proposed method is effective in content-based app recommendation.