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Consistency-Aware Recommendation for User-Generated Item List Continuation

Authors:

James CaverleeAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 250 - 258

https://doi.org/10.1145/3336191.3371776

Published: 22 January 2020 Publication History

Abstract

User-generated item lists are popular on many platforms. Examples include video-based playlists on YouTube, image-based lists (or "boards") on Pinterest, book-based lists on Goodreads, and answer-based lists on question-answer forums like Zhihu. As users create these lists, a common challenge is in identifying what items to curate next. Some lists are organized around particular genres or topics, while others are seemingly incoherent, reflecting individual preferences for what items belong together. Furthermore, this heterogeneity in item consistency may vary from platform to platform, and from sub-community to sub-community. Hence, this paper proposes a generalizable approach for user-generated item list continuation. Complementary to methods that exploit specific content patterns (e.g., as in song-based playlists that rely on audio features), the proposed approach models the consistency of item lists based on human curation patterns, and so can be deployed across a wide range of varying item types (e.g., videos, images, books). A key contribution is in intelligently combining two preference models via a novel consistency-aware gating network -- a general user preference model that captures a user's overall interests, and a current preference priority model that captures a user's current (as of the most recent item) interests. In this way, the proposed consistency-aware recommender can dynamically adapt as user preferences evolve. Evaluation over four datasets (of songs, books, and answers) confirms these observations and demonstrates the effectiveness of the proposed model versus state-of-the-art alternatives. Further, all code and data are available at https://github.com/heyunh2015/ListContinuation_WSDM2020.

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Index Terms

Consistency-Aware Recommendation for User-Generated Item List Continuation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
,
Xia "Ben" Hu
Texas A&M University
,
Program Chairs:
Mounia Lalmas
Spotify
,
Wei Wang
University of California, Los Angeles

Copyright © 2020 ACM.

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Published: 22 January 2020

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WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining

February 3 - 7, 2020

TX, Houston, USA

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Cited By

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https://doi.org/10.1145/3652865
Sun ZFeng KYang JQu XFang HOng YLiu WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Adaptive In-Context Learning with Large Language Models for Bundle GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657808(966-976)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657808
Niu YXing XJia ZLiu RXin MCui JChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Diffusion Recommendation with Implicit Sequence InfluenceCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651951(1719-1725)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651951
Liu QXiao JFan LZhu JWu XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector QuantizationProceedings of the ACM Web Conference 202410.1145/3589334.3645484(3521-3532)Online publication date: 13-May-2024
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Xu ZPan WMing Z(2024)Transfer Learning in Cross-Domain Sequential RecommendationInformation Sciences10.1016/j.ins.2024.120550(120550)Online publication date: Apr-2024
https://doi.org/10.1016/j.ins.2024.120550
Liang Y(2024)Outlier item detection in bundle recommendation via the attention mechanismHigh-Confidence Computing10.1016/j.hcc.2024.1002004:3(100200)Online publication date: Sep-2024
https://doi.org/10.1016/j.hcc.2024.100200
Saito YSugiyama K(2024)Leveraging multiple behaviors and explicit preferences for job recommendationExpert Systems with Applications10.1016/j.eswa.2024.125149258(125149)Online publication date: Dec-2024
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Li MLi LTao XZhong N(2023)User-Meal Interaction Learning for Meal Recommendation: A Reproducibility StudyProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625342(104-113)Online publication date: 26-Nov-2023
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Xu ZPan WMing Z(2023)A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608785(491-501)Online publication date: 14-Sep-2023
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Zhou PYe QXie YGao JWang SKim JYou CKim SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Attention Calibration for Transformer-based Sequential RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614785(3595-3605)Online publication date: 21-Oct-2023
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