Leveraging Long and Short-term Information in Content-aware Movie Recommendation

TL;DR

This paper introduces LSIC, a novel content-aware movie recommendation model that combines long-term and short-term user preferences using adversarial training, improving recommendation accuracy especially with limited data.

Contribution

The paper proposes a new adversarial training framework that integrates long-term and short-term information with movie content for enhanced recommendations.

Findings

LSIC outperforms existing models in accuracy.

Incorporating poster information improves performance with sparse data.

The adversarial approach enhances recommendation robustness.

Abstract

Movie recommendation systems provide users with ranked lists of movies based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term models represent the interactions between users and movies that are supposed to change slowly across time, session-based models encode the information of users' interests and changing dynamics of movies' attributes in short terms. In this paper, we propose an LSIC model, leveraging Long and Short-term Information in Content-aware movie recommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next movie to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of movies from the real records. The poster information of movies is integrated to further improve the performance of movie recommendation, which is specifically essential when few ratings are available. The experiments demonstrate that the proposed model has robust superiority over competitors and sets the state-of-the-art. We will release the source code of this work after publication.