research-article

Pairwise preference regression for cold-start recommendation

Authors:

Seung-Taek Park,

Wei ChuAuthors Info & Claims

RecSys '09: Proceedings of the third ACM conference on Recommender systems

Pages 21 - 28

https://doi.org/10.1145/1639714.1639720

Published: 23 October 2009 Publication History

Abstract

Recommender systems are widely used in online e-commerce applications to improve user engagement and then to increase revenue. A key challenge for recommender systems is providing high quality recommendation to users in ``cold-start" situations. We consider three types of cold-start problems: 1) recommendation on existing items for new users; 2) recommendation on new items for existing users; 3) recommendation on new items for new users. We propose predictive feature-based regression models that leverage all available information of users and items, such as user demographic information and item content features, to tackle cold-start problems. The resulting algorithms scale efficiently as a linear function of the number of observations. We verify the usefulness of our approach in three cold-start settings on the MovieLens and EachMovie datasets, by comparing with five alternatives including random, most popular, segmented most popular, and two variations of Vibes affinity algorithm widely used at Yahoo! for recommendation.

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Cited By

Siet SPeng SIlkhomjon SKang MPark D(2024)Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeansApplied Sciences10.3390/app1406250514:6(2505)Online publication date: 15-Mar-2024
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Liu SZhang YYi LDeng XYang LWang B(2024)Dual-Side Adversarial Learning Based Fair Recommendation for Sensitive Attribute FilteringACM Transactions on Knowledge Discovery from Data10.1145/364868318:7(1-20)Online publication date: 19-Feb-2024
https://dl.acm.org/doi/10.1145/3648683
Liu HZhou MSong MOuyang DLi YJing LYu JNg M(2024)Learning Hierarchical Preferences for Recommendation with Mixture Intention Neural Stochastic ProcessesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3348493(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3348493
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Pairwise preference regression for cold-start recommendation

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Published In

cover image ACM Conferences

RecSys '09: Proceedings of the third ACM conference on Recommender systems

October 2009

442 pages

ISBN:9781605584355

DOI:10.1145/1639714

General Chairs:
Lawrence Bergman
IBM Research, NY, USA
,
Alex Tuzhilin
New York University, NY, USA
,
Program Chairs:
Robin Burke
DePaul University, USA
,
Alexander Felfernig
Graz University of Technology, Austria
,
Lars Schmidt-Thieme
University of Hildesheim, Germany

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2009

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RecSys '09

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SIGCHI

RecSys '09: Third ACM Conference on Recommender Systems

October 23 - 25, 2009

New York, New York, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Siet SPeng SIlkhomjon SKang MPark D(2024)Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeansApplied Sciences10.3390/app1406250514:6(2505)Online publication date: 15-Mar-2024
https://doi.org/10.3390/app14062505
Liu SZhang YYi LDeng XYang LWang B(2024)Dual-Side Adversarial Learning Based Fair Recommendation for Sensitive Attribute FilteringACM Transactions on Knowledge Discovery from Data10.1145/364868318:7(1-20)Online publication date: 19-Feb-2024
https://dl.acm.org/doi/10.1145/3648683
Liu HZhou MSong MOuyang DLi YJing LYu JNg M(2024)Learning Hierarchical Preferences for Recommendation with Mixture Intention Neural Stochastic ProcessesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3348493(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3348493
Lin XZhou CWu JZou LPan SCao YWang BWang SYin D(2024)Towards Flexible and Adaptive Neural Process for Cold-Start RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330483936:4(1815-1828)Online publication date: Apr-2024
https://doi.org/10.1109/TKDE.2023.3304839
Dash PSaha AMaurya HGupta SMukherjee S(2024)Recommendation-based Smart Shopping System2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.010.1109/OTCON60325.2024.10687654(1-6)Online publication date: 5-Jun-2024
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Mallea MÑanculef RParra D(2024)Adversarial Pairwise Multimodal Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650977(1-10)Online publication date: 30-Jun-2024
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Raj SSharma ASaha SSingh BPedanekar N(2024)Transformative Movie Discovery: Large Language Models for Recommendation and Genre PredictionIEEE Access10.1109/ACCESS.2024.348246112(186626-186638)Online publication date: 2024
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Pu JWang YNan FLiu X(2024)AdaMO: Adaptive Meta-Optimization for cold-start recommendationNeurocomputing10.1016/j.neucom.2024.127417580(127417)Online publication date: May-2024
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Zhou XLin NZheng WZhou DYang A(2024)A contrastive news recommendation framework based on curriculum learningUser Modeling and User-Adapted Interaction10.1007/s11257-024-09422-035:1Online publication date: 28-Dec-2024
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Oliveira CJames LKhanshan AGorp P(2024)Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior ChangeProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-5035-1_49(623-634)Online publication date: 23-Oct-2024
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