research-article

Latent Factor Representations for Cold-Start Video Recommendation

Authors:

Sharath Chandra GuntukuAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 99 - 106

https://doi.org/10.1145/2959100.2959172

Published: 07 September 2016 Publication History

Abstract

Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary information for learning latent factor representations. In this work we present a novel method for learning latent factor representation for videos based on modelling the emotional connection between user and item. First of all we present a comparative analysis of state-of-the art emotion modelling approaches that brings out a surprising finding regarding the efficacy of latent factor representations in modelling emotion in video content. Based on this finding we present a method visual-CLiMF for learning latent factor representations for cold start videos based on implicit feedback. Visual-CLiMF is based on the popular collaborative less-is-more approach but demonstrates how emotional aspects of items could be used as auxiliary information to improve MRR performance. Experiments on a new data set and the Amazon products data set demonstrate the effectiveness of visual-CLiMF which outperforms existing CF methods with or without content information.

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Cited By

Jangid MKumar R(2024)Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic reviewMultimedia Tools and Applications10.1007/s11042-024-20262-3Online publication date: 16-Oct-2024
https://doi.org/10.1007/s11042-024-20262-3
Tejón RSanguino JManrique R(2024)MultiMF: A Deep Multimodal Academic Resources Recommendation SystemApplied Informatics10.1007/978-3-031-75144-8_7(89-104)Online publication date: 19-Oct-2024
https://doi.org/10.1007/978-3-031-75144-8_7
ÖZ AUZUN-PER MBAL M(2023)Kullanıcı ve Öğe Bazlı, Geniş ve Derin Öğrenme Tabanlı Seyahat Öneri SistemiA User and Item-Based, Wide and Deep Learning Based Travel Recommendation SystemEuropean Journal of Science and Technology10.31590/ejosat.1296379Online publication date: 25-Aug-2023
https://doi.org/10.31590/ejosat.1296379
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Index Terms

Latent Factor Representations for Cold-Start Video Recommendation

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Published In

cover image ACM Conferences

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

Copyright © 2016 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: 07 September 2016

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Economic Development Board Singapore
National Research Foundation Singapore

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RecSys '16

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SIGCHI

RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

Acceptance Rates

RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

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

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Cited By

Jangid MKumar R(2024)Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic reviewMultimedia Tools and Applications10.1007/s11042-024-20262-3Online publication date: 16-Oct-2024
https://doi.org/10.1007/s11042-024-20262-3
Tejón RSanguino JManrique R(2024)MultiMF: A Deep Multimodal Academic Resources Recommendation SystemApplied Informatics10.1007/978-3-031-75144-8_7(89-104)Online publication date: 19-Oct-2024
https://doi.org/10.1007/978-3-031-75144-8_7
ÖZ AUZUN-PER MBAL M(2023)Kullanıcı ve Öğe Bazlı, Geniş ve Derin Öğrenme Tabanlı Seyahat Öneri SistemiA User and Item-Based, Wide and Deep Learning Based Travel Recommendation SystemEuropean Journal of Science and Technology10.31590/ejosat.1296379Online publication date: 25-Aug-2023
https://doi.org/10.31590/ejosat.1296379
Wang CZhu YLiu HZang TWang KYu J(2023)Multifaceted Relation-aware Meta-learning with Dual Customization for User Cold-start RecommendationACM Transactions on Knowledge Discovery from Data10.1145/359745817:9(1-27)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3597458
Liu HLin HZhang XMa FChen HWang LYu HZhang XFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Boosting Meta-Learning Cold-Start Recommendation with Graph Neural NetworkProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615283(4105-4109)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615283
Yang WFang ZZhang TWu SLu CEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Modal-aware Bias Constrained Contrastive Learning for Multimodal RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612568(6369-6378)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612568
Wu ZZhou XChen HDuh WHuang HKato MMothe JPoblete B(2023)M2EU: Meta Learning for Cold-start Recommendation via Enhancing User Preference EstimationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591719(1158-1167)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591719
Wang CZhu YSun AWang ZWang KChen HDuh WHuang HKato MMothe JPoblete B(2023)A Preference Learning Decoupling Framework for User Cold-Start RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591627(1168-1177)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591627
Li YZhang YChang HLi Q(2023)Injecting Revenue-awareness into Cold-start Recommendation: The Case of Online Insurance2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00199(1397-1404)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ICPADS60453.2023.00199
Wang DZhao X(2022)Affective video recommender systems: A surveyFrontiers in Neuroscience10.3389/fnins.2022.98440416Online publication date: 26-Aug-2022
https://doi.org/10.3389/fnins.2022.984404
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