# Personalized Video Recommendation Using Rich Contents from Videos

Source: [Personalized Video Recommendation Using Rich Contents from Videos](<https://www.sota2.com/research/paper/68fa651c1e209131cea0867e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:19:52.933Z

Authors: Xingzhong Du, Hongzhi Yin, Ling Chen, Yang Wang, Yi Yang, Xiaofang Zhou

Published: 2016-12-21T01:01:49.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Recommender Systems on MovieLens item 10M (cold-start)](<https://www.sota2.com/research/sota/recommender-systems-on-movielens-item-10m-cold-start.md>): Recommender Systems; MovieLens item 10M (cold-start); Div. SE @5: 9.1818.

- [Video Recommendation on MovieLens-10M item warm-start scenario](<https://www.sota2.com/research/sota/video-recommendation-on-movielens-10m-item-warm-start-scenario.md>): Video Recommendation; MovieLens-10M item warm-start scenario; Shannon Entropy @5: 8.0829.

- [Video Recommendation on MovieLens 10M (item cold-start)](<https://www.sota2.com/research/sota/video-recommendation-on-movielens-10m-item-cold-start.md>): Video Recommendation; MovieLens 10M (item cold-start); MAP@5: 0.0159.

- [Video Recommendation on MovieLens item warm-start 10M](<https://www.sota2.com/research/sota/video-recommendation-on-movielens-item-warm-start-10m.md>): Video Recommendation; MovieLens item warm-start 10M; MAP@5: 0.1113.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1612.06935>)

[PDF](<https://arxiv.org/pdf/1612.06935.pdf>)