research-article

Leveraging user libraries to bootstrap collaborative filtering

Authors:

Laurent Charlin,

Richard S. Zemel,

Hugo LarochelleAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 173 - 182

https://doi.org/10.1145/2623330.2623663

Published: 24 August 2014 Publication History

Abstract

We introduce a novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents. CSTM's chief novelty lies in its learned model of individual libraries, or sets of documents, associated with each user. Overall, CSTM is a joint directed probabilistic model of user-item scores (ratings), and the textual side information in the user libraries and the items. Creating a generative description of scores and the text allows CSTM to perform well in a wide variety of data regimes, smoothly combining the side information with observed ratings as the number of ratings available for a given user ranges from none to many. Experiments on real-world datasets demonstrate CSTM's performance. We further demonstrate its utility in an application for personal recommendations of posters which we deployed at the NIPS 2013 conference.

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Cited By

Chen WZhang YXian YWen Y(2023)Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature RecommendationApplied Sciences10.3390/app1302109313:2(1093)Online publication date: 13-Jan-2023
https://doi.org/10.3390/app13021093
Zhou YLiu LLee KPalanisamy BZhang Q(2020)Improving Collaborative Filtering with Social Influence over Heterogeneous Information NetworksACM Transactions on Internet Technology10.1145/339750520:4(1-29)Online publication date: 15-Oct-2020
https://dl.acm.org/doi/10.1145/3397505
Li XChen YPettit BRijke M(2019)Personalised Reranking of Paper Recommendations Using Paper Content and User BehaviorACM Transactions on Information Systems10.1145/331252837:3(1-23)Online publication date: 16-Mar-2019
https://dl.acm.org/doi/10.1145/3312528
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Leveraging user libraries to bootstrap collaborative filtering

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Chen WZhang YXian YWen Y(2023)Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature RecommendationApplied Sciences10.3390/app1302109313:2(1093)Online publication date: 13-Jan-2023
https://doi.org/10.3390/app13021093
Zhou YLiu LLee KPalanisamy BZhang Q(2020)Improving Collaborative Filtering with Social Influence over Heterogeneous Information NetworksACM Transactions on Internet Technology10.1145/339750520:4(1-29)Online publication date: 15-Oct-2020
https://dl.acm.org/doi/10.1145/3397505
Li XChen YPettit BRijke M(2019)Personalised Reranking of Paper Recommendations Using Paper Content and User BehaviorACM Transactions on Information Systems10.1145/331252837:3(1-23)Online publication date: 16-Mar-2019
https://dl.acm.org/doi/10.1145/3312528
Vartak MThiagarajan AMiranda CBratman JLarochelle H(2017)A meta-learning perspective on cold-start recommendations for itemsProceedings of the 31st International Conference on Neural Information Processing Systems10.5555/3295222.3295434(6907-6917)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.5555/3295222.3295434
Halder KKan MSugiyama KLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Health Forum Thread Recommendation Using an Interest Aware Topic ModelProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132946(1589-1598)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132946
Zheng LChen L(2016)Mutual benefit aware task assignment in a bipartite labor market2016 IEEE 32nd International Conference on Data Engineering (ICDE)10.1109/ICDE.2016.7498230(73-84)Online publication date: May-2016
https://doi.org/10.1109/ICDE.2016.7498230
Bansal TDas MBhattacharyya CWerthner HZanker MGolbeck JSemeraro G(2015)Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog ArticlesProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2800186(195-202)Online publication date: 16-Sep-2015
https://dl.acm.org/doi/10.1145/2792838.2800186

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