A Deep, Forgetful Novelty-Seeking Movie Recommender Model

TL;DR

This paper introduces a novel deep learning model that incorporates user forgetfulness and novelty-seeking traits, improving the accuracy of personalized movie recommendations by leveraging demographic and genre data.

Contribution

It proposes the Deep Forgetful Novelty-Seeking Model (DFNSM), a new approach that models user forgetfulness and novelty-seeking traits for enhanced recommendation accuracy.

Findings

DFNSM outperforms existing models on large movie rating datasets.

Incorporating forgetfulness improves prediction accuracy.

Model effectively captures user novelty-seeking behavior.

Abstract

As more and more people shift their movie watching online, competition between movie viewing websites are getting more and more intense. Therefore, it has become incredibly important to accurately predict a given user's watching list to maximize the chances of keeping the user on the platform. Recent studies have suggested that the novelty-seeking propensity of users can impact their viewing behavior. In this paper, we aim to accurately model and describe this novelty-seeking trait across many users and timestamps driven by data, taking into consideration user forgetfulness. Compared to previous studies, we propose a more robust measure for novelty. Our model, termed Deep Forgetful Novelty-Seeking Model (DFNSM), leverages demographic information about users, genre information about movies, and novelty-seeking traits to predict the most likely next actions of a user. To evaluate the performance of our model, we conducted extensive experiments on a large movie rating dataset. The results reveal that DFNSM is very effective for movie recommendation.