EX3: Explainable attribute-aware item-set recommendations

Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, X. Ein Luna Dong, Christos Faloutsos, George Karypis, S. Muthukrishnan, Yongfeng Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Existing recommender systems in the e-commerce domain primarily focus on generating a set of relevant items as recommendations; however, few existing systems utilize underlying item attributes as a key organizing principle in presenting recommendations to users. Mining important attributes of items from customer perspectives and presenting them along with item sets as recommendations can provide users more explainability and help them make better purchase decision. In this work, we generalize the attribute-aware item-set recommendation problem, and develop a new approach to generate sets of items (recommendations) with corresponding important attributes (explanations) that can best justify why the items are recommended to users. In particular, we propose a system that learns important attributes from historical user behavior to derive item set recommendations, so that an organized view of recommendations and their attribute-driven explanations can help users more easily understand how the recommendations relate to their preferences. Our approach is geared towards real world scenarios: we expect a solution to be scalable to billions of items, and be able to learn item and attribute relevance automatically from user behavior without human annotations. To this end, we propose a multi-step learning-based framework called Extract-Expect-Explain (EX3), which is able to adaptively select recommended items and important attributes for users. We experiment on a large-scale real-world benchmark and the results show that our model outperforms state-of-the-art baselines by an 11.35% increase on NDCG with adaptive explainability for item set recommendation.

Original languageEnglish (US)
Title of host publicationRecSys 2021 - 15th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages484-494
Number of pages11
ISBN (Electronic)9781450384582
DOIs
StatePublished - Sep 13 2021
Externally publishedYes
Event15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online, Netherlands
Duration: Sep 27 2021Oct 1 2021

Publication series

NameRecSys 2021 - 15th ACM Conference on Recommender Systems

Conference

Conference15th ACM Conference on Recommender Systems, RecSys 2021
Country/TerritoryNetherlands
CityVirtual, Online
Period9/27/2110/1/21

Bibliographical note

Publisher Copyright:
© 2021 ACM.

Keywords

  • Explainable recommendation
  • Item set recommendation
  • Recommender system

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