research-article

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Improving User Topic Interest Profiles by Behavior Factorization

Authors:

Ed H. ChiAuthors Info & Claims

WWW '15: Proceedings of the 24th International Conference on World Wide Web

Pages 1406 - 1416

https://doi.org/10.1145/2736277.2741656

Published: 18 May 2015 Publication History

Abstract

Many recommenders aim to provide relevant recommendations to users by building personal topic interest profiles and then using these profiles to find interesting contents for the user. In social media, recommender systems build user profiles by directly combining users' topic interest signals from a wide variety of consumption and publishing behaviors, such as social media posts they authored, commented on, +1'd or liked. Here we propose to separately model users' topical interests that come from these various behavioral signals in order to construct better user profiles. Intuitively, since publishing a post requires more effort, the topic interests coming from publishing signals should be more accurate of a user's central interest than, say, a simple gesture such as a +1. By separating a single user's interest profile into several behavioral profiles, we obtain better and cleaner topic interest signals, as well as enabling topic prediction for different types of behavior, such as topics that the user might +1 or comment on, but might never write a post on that topic.

To do this at large scales in Google+, we employed matrix factorization techniques to model each user's behaviors as a separate example entry in the input user-by-topic matrix. Using this technique, which we call "behavioral factorization", we implemented and built a topic recommender predicting user's topical interests using their actions within Google+. We experimentally showed that we obtained better and cleaner signals than baseline methods, and are able to more accurately predict topic interests as well as achieve better coverage.

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Cited By

Meng ZYi ZOunis I(2024)Knowledge-Enhanced Multi-Behaviour Contrastive Learning for Effective RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688186(1016-1021)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688186
Yan MLiu FSun JSun FCheng ZHan YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Behavior-Contextualized Item Preference Modeling for Multi-Behavior RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657696(946-955)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657696
Han YWang HWang KWu LLi ZGuo WLiu YLian DChen EChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Efficient Noise-Decoupling for Multi-Behavior Sequential RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645380(3297-3306)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645380
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Index Terms

Improving User Topic Interest Profiles by Behavior Factorization
1. Information systems
  1. Information systems applications
    1. Data mining

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Reviews

Reviewer: A. Squassabia

Do the topic preferences of social media users differ between producing and consuming content__?__ And if so, do recommenders currently account for such differences__?__ This paper acknowledges that the difference is real and measurable, and exploits that finding to report for the first time (as the study claims) how to build a specialized recommender that produces better results when evaluated with ranking metrics. The experimental set is from Google Plus user data collected during May and June 2014. This data is preprocessed to extract higher-level semantic concepts, taking advantage of the Google Knowledge Graph. Processing occurs using cascading engines that assess users' interests in different topics (roughly, the higher-level concepts) using matrix factorization, specializing for behaviors (for example, publish or consume), and capturing interest for combinations of the above. The resulting improvement, evaluated with metrics of recall, normalized discounted cumulative gain, and average percentile metrics, is reportedly impressive: usually 30 percent better (or more) than standard (that is, topic-unaware) results. Interestingly, the work does not report any prediction metrics (for example, mean square error); however, it could be argued that in this context prediction has little meaning. Preprocessing raw data into semantic topics using Google's in-house ontology will be laborious to replicate for an independent confirmation, even if essential to the core impact of this approach. The reported improvement, in any case, suggests that user behavior specialized by multiple circumstances, creating multiple customized profiles, is key to computing better recommendations. Online Computing Reviews Service

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Information & Contributors

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Published In

cover image ACM Other conferences

WWW '15: Proceedings of the 24th International Conference on World Wide Web

May 2015

1460 pages

ISBN:9781450334693

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Copyright © 2015 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 18 May 2015

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WWW '15

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WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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WWW '15 Paper Acceptance Rate 131 of 929 submissions, 14%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Meng ZYi ZOunis I(2024)Knowledge-Enhanced Multi-Behaviour Contrastive Learning for Effective RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688186(1016-1021)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688186
Yan MLiu FSun JSun FCheng ZHan YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Behavior-Contextualized Item Preference Modeling for Multi-Behavior RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657696(946-955)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657696
Han YWang HWang KWu LLi ZGuo WLiu YLian DChen EChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Efficient Noise-Decoupling for Multi-Behavior Sequential RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645380(3297-3306)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645380
Kayed MAzzam FAli HAli A(2024)Temporal dynamics of user activities: deep learning strategies and mathematical modeling for long-term and short-term profilingScientific Reports10.1038/s41598-024-64120-614:1Online publication date: 24-Jun-2024
https://doi.org/10.1038/s41598-024-64120-6
Hao QWang CXiao YLin H(2024)Simplices-based higher-order enhancement graph neural network for multi-behavior recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10379061:5Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ipm.2024.103790
Zarrinkalam FNoughabi HNoorian ZFani HBagheri E(2024)Predicting users’ future interests on social networks: A reference frameworkInformation Processing & Management10.1016/j.ipm.2024.10376561:5(103765)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103765
Zhang ZJia YHou YYu X(2024)Explicit Behavior Interaction with Heterogeneous Graph for Multi-behavior RecommendationData Science and Engineering10.1007/s41019-023-00238-39:2(133-151)Online publication date: 19-Jan-2024
https://doi.org/10.1007/s41019-023-00238-3
Zhang KChu DTu ZLiu XZhang B(2024)LSTM-UBI: a user behavior inertia based recommendation methodMultimedia Tools and Applications10.1007/s11042-024-18256-283:27(69227-69248)Online publication date: 31-Jan-2024
https://doi.org/10.1007/s11042-024-18256-2
Zhang W(2023)Application and Influence of Conditioned Reflex Theory in Interactive DeviceJournal of Education, Humanities and Social Sciences10.54097/ehss.v22i.1422522(810-816)Online publication date: 26-Nov-2023
https://doi.org/10.54097/ehss.v22i.14225
Qu MWang NLi J(2023)A Two-Path Multibehavior Model of User InteractionElectronics10.3390/electronics1214304812:14(3048)Online publication date: 12-Jul-2023
https://doi.org/10.3390/electronics12143048
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