research-article

Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation

Authors:

Rakshit Trivedi,

Le SongAuthors Info & Claims

DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems

Pages 29 - 34

https://doi.org/10.1145/2988450.2988451

Published: 15 September 2016 Publication History

Abstract

Matching users to the right items at the right time is a fundamental task in recommender systems. As users interact with different items over time, users' and items' feature may drift, evolve and co-evolve over time. Traditional models based on static latent features or discretizing time into epochs can become ineffective for capturing the fine-grained temporal dynamics in the user-item interactions. We propose a coevolutionary latent feature process model that accurately captures the coevolving nature of users' and items' feature. We use a recurrent neural network to automatically learn a representation of influences from drift, evolution and co-evolution of user and item features. We develop an efficient stochastic gradient algorithm for learning the model parameters which can readily scale up to millions of events. Experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.

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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/s11042-024-20262-384:5(2293-2325)Online publication date: 16-Oct-2024
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Published In

cover image ACM Other conferences

DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems

September 2016

47 pages

ISBN:9781450347952

DOI:10.1145/2988450

Conference Chairs:
Alexandros Karatzoglou
Telefonica Research
,
Balázs Hidasi
Gravity Research & Development
,
Domonkos Tikk
Gravity Research Development
,
Oren Sar-Shalom
IBM Research
,
Haggai Roitman
IBM Research
,
Bracha Shapira
Ben-Gurion University
,
Lior Rokach
Ben-Gurion University

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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IBMR: IBM Research

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New York, NY, United States

Publication History

Published: 15 September 2016

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DLRS 2016: Workshop on Deep Learning for Recommender Systems

September 15, 2016

MA, Boston, USA

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https://doi.org/10.1016/j.rcim.2024.102898
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/s11042-024-20262-384:5(2293-2325)Online publication date: 16-Oct-2024
https://doi.org/10.1007/s11042-024-20262-3
Qin ZZhang M(2023)A Movie Recommendation Algorithm Combining Time Weight GRU and Attention2023 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)10.1109/AEECA59734.2023.00154(839-844)Online publication date: 18-Aug-2023
https://doi.org/10.1109/AEECA59734.2023.00154
Xiao JLiu XZeng JFeng ZCao Y(2022)Recommendation of Healthcare Services Based on an Embedded User Profile ModelInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.31319818:1(1-21)Online publication date: 27-Oct-2022
https://dl.acm.org/doi/10.4018/IJSWIS.313198
Uta MFelfernig AHelic DLe VFelfernig AFuentes L(2022)Accuracy- and consistency-aware recommendation of configurationsProceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A10.1145/3546932.3546996(79-84)Online publication date: 12-Sep-2022
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Oh SUstun BMcAuley JKumar SAl Hasan MXiong L(2022)Rank List Sensitivity of Recommender Systems to Interaction PerturbationsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557425(1584-1594)Online publication date: 17-Oct-2022
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Nie PLu YZhang SZhao MXie RWang WRen YAl Hasan MXiong L(2022)MIC: Model-agnostic Integrated Cross-channel RecommenderProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557081(3400-3409)Online publication date: 17-Oct-2022
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Wu LHe XWang XZhang KWang M(2022)A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3145690(1-1)Online publication date: 2022
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Hu QFeng ZXue DCai MYu J(2022)Towards An Integrated Framework for Neural Temporal Point Process2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892964(1-8)Online publication date: 18-Jul-2022
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