Personalized Video Recommendation Using Rich Contents from Videos

TL;DR

This paper introduces a versatile framework for video recommendation that leverages rich video contents like text and audio, improving accuracy especially when specific features are missing, and enhances performance through feature fusion.

Contribution

The paper proposes a novel collaborative embedding regression model that effectively utilizes arbitrary rich content features for video recommendation, addressing limitations of existing models.

Findings

CER outperforms existing models with any single content feature

CER is more time efficient than baseline models

PRI fusion method improves recommendation performance

Abstract

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.