research-article

Tied boltzmann machines for cold start recommendations

Authors:

Asela Gunawardana,

Christopher MeekAuthors Info & Claims

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

Pages 19 - 26

https://doi.org/10.1145/1454008.1454013

Published: 23 October 2008 Publication History

Abstract

We describe a novel statistical model, the tied Boltzmann machine, for combining collaborative and content information for recommendations. In our model, pairwise interactions between items are captured through a Boltzmann machine, whose parameters are constrained according to the content associated with the items. This allows the model to use content information to recommend items that are not seen during training. We describe a tractable algorithm for training the model, and give experimental results evaluating the model in two cold start recommendation tasks on the MovieLens data set.

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Cited By

Aljunid MD.H. MHooshmand MAli WShetty AAlzoubah S(2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128718
Jin JChen XYe FYang MFeng YZhang WYu YWang JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Lending interaction wings to recommender systems with conversational agentsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667335(27951-27979)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667335
Jin XZhou WWang JXU DZheng YEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612140(6938-6947)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612140
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Information

Published In

cover image ACM Conferences

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

October 2008

348 pages

ISBN:9781605580937

DOI:10.1145/1454008

General Chair:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
,
Program Chairs:
Derek Bridge
University College Cork, Ireland
,
Bamshad Mobasher
DePaul University, USA
,
Francesco Ricci
Free University Bozen-Bolzano, Italy

Copyright © 2008 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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Publication History

Published: 23 October 2008

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RecSys08: ACM Conference on Recommender Systems

October 23 - 25, 2008

Lausanne, Switzerland

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

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Cited By

Aljunid MD.H. MHooshmand MAli WShetty AAlzoubah S(2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128718
Jin JChen XYe FYang MFeng YZhang WYu YWang JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Lending interaction wings to recommender systems with conversational agentsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667335(27951-27979)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667335
Jin XZhou WWang JXU DZheng YEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612140(6938-6947)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612140
Chen XLu YWang YYang J(2021)CMBF: Cross-Modal-Based Fusion Recommendation AlgorithmSensors10.3390/s2116527521:16(5275)Online publication date: 4-Aug-2021
https://doi.org/10.3390/s21165275
Choi SLee DPark C(2020)On the Smaller Number of Inputs for Determining User Preferences in Recommender SystemsMathematics10.3390/math81221388:12(2138)Online publication date: 1-Dec-2020
https://doi.org/10.3390/math8122138
Chen WHsu CLai YLiu VYeh MLin S(2020)Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and ClassificationFrontiers in Big Data10.3389/fdata.2019.000492Online publication date: 15-Jan-2020
https://doi.org/10.3389/fdata.2019.00049
Zhu YLin JHe SWang BGuan ZLiu HCai D(2020)Addressing the Item Cold-Start Problem by Attribute-Driven Active LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289153032:4(631-644)Online publication date: 1-Apr-2020
https://doi.org/10.1109/TKDE.2019.2891530
Choi SJang KLee TKhreishah ANoh W(2020)Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak SupervisionIEEE Access10.1109/ACCESS.2020.30194648(167747-167756)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3019464
Khan ZNiu ZSandiwarno SPrince R(2020)Deep learning techniques for rating prediction: a survey of the state-of-the-artArtificial Intelligence Review10.1007/s10462-020-09892-9Online publication date: 19-Aug-2020
https://doi.org/10.1007/s10462-020-09892-9
Zhou JChiky R(2019)Improving the Attribute-Based Active Learning by Clustering the New Items2019 IEEE World Congress on Services (SERVICES)10.1109/SERVICES.2019.00095(343-344)Online publication date: Jul-2019
https://doi.org/10.1109/SERVICES.2019.00095
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