Abstract
As the rapid development of mobile Internet and smart devices, more and more online content providers begin to collect the preferences of their customers through various apps on mobile devices. These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user. Based on the empirical analysis of three real-world movie-rating data sets, we observe that users’ rating criterions change over time, and past positive and negative ratings have different influences on users’ future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings. The extensive experiment results validate the significant accuracy improvement of our proposed model compared with the state-of-the-art methods.
Highlights
Nowadays, a huge ecosystem of independent content providers and consumers is emerging on the mobile Internet
Recommender systems have received considerable research attention in the literature, and many effective recommendation approaches have been proposed, such as social network-based recommendation models [3], graphbased recommendation models [4, 5], and context aware recommendation models [6, 7]; a recent and up-to-date review can be found in the works of Lu et al [8]
Based on the session-based temporal graph (STG) introduced by Xiang et al [4], we propose a session-based recommendation model with the temporal effect of user preferences (STeuP), which is an enhanced version of the Injected Preference Fusion (IPF) model associated with STG
Summary
A huge ecosystem of independent content providers (such as Facebook, Netflix, Google Maps, and Snapchat) and consumers (web users) is emerging on the mobile Internet. In online video-watching websites with recommender systems, users are asked to rate movies with discrete scores to express their individual opinions, where a high score usually indicates user preference on this movie. Take https://www.netflix.com as an example, users are suggested to rate movies and TV shows (items in general) in a rating scale from 1 star to 5 stars, where one star means “Hate It,” and five stars mean “Love It.” This kind of explicit feedbacks can largely reflect user preferences. As the mobile platforms become more and more user friendly, computationally powerful, and readily available, online content providers have begun to develop mobile apps to offer more personalized contents People can watch their favorite movies and TV shows wherever and whenever they have a break. Compared with five state-of-the-art methods on the aforementioned movie-rating date sets, our proposed model is validated to give more accurate prediction of user preference
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