Bayesian Approach for Eliminating User Influence and Item Popularity Biases in User-Item Bipartite Networks

• 발행기관: 한국자료분석학회
• 저자명: Hohyun Jung
• 간행물 정보: 『한국자료분석학회 학술대회자료집』2022년 하계학술대회 발표집, 123~125쪽, 전체 3쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2022.07.07

영문 초록

User-item bipartite networks consist of users and items, where edges indicate the interactions of user-item pairs. We propose a Bayesian generative model to the user-item bipartite network that can measure the two types of rich-get-richer biases: item popularity and user influence biases. Furthermore, the model contains a novel measure of an item, namely the item quality that can be used in the item recommender system. The item quality represents the genuine worth of an item when the biases are removed. We apply the proposed model to Flickr user-tag and Netflix user-movie networks to yield remarkable interpretations of the rich-get-richer biases. We further discuss genuine item quality using Flickr tags and Netflix movies, considering the importance of bias elimination.

목차

1. Introduction

2. Methods

3. Results

4. Conclusion

References

키워드

Bayesian inference bipartite networks item recommendation rich-get-richer social network