Portrait de Haolun Wu

Haolun Wu

Doctorat - McGill
Superviseur⋅e principal⋅e
Xue (Steve) Liu
Co-supervisor
Fernando Diaz
Sujets de recherche
Apprentissage profond
Exploration des données
Recherche d'information
Systèmes de recommandation
Traitement du langage naturel
Site web
Google Scholar

Publications

A Survey of Diversification Metrics and Approaches in Retrieval Systems: From the Perspective of Search and Recommendation
Haolun Wu
Yansen Zhang
Chen Ma
Fuyuan Lyu
Fernando Diaz
Xue Liu
Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (voir plus)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems
2022-12-31
(publié)
www.semanticscholar.org
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Haolun Wu
Chen Ma
Yingxue Zhang
Xue Liu
Ruiming Tang
Mark J. Coates
2022-10-16
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (publié)
doi.org
arxiv.org
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu
Bhaskar Mitra
Chen Ma
Fernando Diaz
Xue Liu
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or… (voir plus) groups of items to individual users of the system. The problem of how individual or groups of items may be systemically under or over exposed to groups of users, or even all users, has received relatively less attention. However, such systemic disparities in information exposure can result in observable social harms, such as withholding economic opportunities from historically marginalized groups (allocative harm) or amplifying gendered and racialized stereotypes (representational harm). Previously, Diaz et al. developed the expected exposure metric---that incorporates existing user browsing models that have previously been developed for information retrieval---to study fairness of content exposure to individual users. We extend their proposed framework to formalize a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation. Furthermore, we study and discuss the relationships between the different exposure fairness dimensions proposed in this paper, as well as demonstrate how stochastic ranking policies can be optimized towards said fairness goals.
2022-07-06
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (publié)
doi.org
arxiv.org