Enhancing Live Broadcast Engagement: A Multi-modal Approach to Short Video Recommendations Using MMGCN and User Preferences

TL;DR

This paper presents a multi-modal graph convolutional network approach that improves personalized short video recommendations by integrating user preferences, content features, and contextual data, leading to enhanced live broadcast engagement.

Contribution

It introduces a novel MMGCN-based recommendation system that effectively combines multi-modal data and user preferences, outperforming traditional models in live broadcast content recommendation.

Findings

Outperforms baseline models like DeepFM and XGBoost in F1 scores.

Effectively captures diverse user preferences for personalized recommendations.

Demonstrates the importance of multi-modal integration in recommender systems.

Abstract

The purpose of this paper is to explore a multi-modal approach to enhancing live broadcast engagement by developing a short video recommendation system that incorporates Multi-modal Graph Convolutional Networks (MMGCN) with user preferences. To provide personalized recommendations tailored to individual interests, the proposed system considers user interaction data, video content features, and contextual information. With the aid of a hybrid approach combining collaborative filtering and content-based filtering techniques, the system can capture nuanced relationships between users, video attributes, and engagement patterns. Three datasets are used to evaluate the effectiveness of the system: Kwai, TikTok, and MovieLens. Compared to baseline models, such as DeepFM, Wide & Deep, LightGBM, and XGBoost, the proposed MMGCN-based model shows superior performance. A notable feature of the proposed model is that it outperforms all baseline methods in capturing diverse user preferences and making accurate, personalized recommendations, resulting in a Kwai F1 score of 0.574, a Tiktok F1 score of 0.506, and a MovieLens F1 score of 0.197. We emphasize the importance of multi-modal integration and user-centric approaches in advancing recommender systems, emphasizing the role they play in enhancing content discovery and audience interaction on live broadcast platforms.