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Coevolutionary latent feature processes for continuous-time user-item interactions

Authors:

Rakshit Trivedi,

Le SongAuthors Info & Claims

NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems

Pages 4554 - 4562

Published: 05 December 2016 Publication History

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Abstract

Matching users to the right items at the right time is a fundamental task in recommendation systems. As users interact with different items over time, users' and items' feature may 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. To learn parameters, we design an efficient convex optimization algorithm with a novel low rank space sharing constraints. Extensive experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts.

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Cited By

You JWang YPal AEksombatchai PRosenburg CLeskovec J(2019)Hierarchical Temporal Convolutional Networks for Dynamic Recommender SystemsThe World Wide Web Conference10.1145/3308558.3313747(2236-2246)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313747
Song QChang SHu XTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Coupled Variational Recurrent Collaborative FilteringProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330940(335-343)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330940
Kumar SZhang XLeskovec JTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Predicting Dynamic Embedding Trajectory in Temporal Interaction NetworksProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330895(1269-1278)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330895
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Published In

cover image Guide Proceedings

NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems

December 2016

5100 pages

ISBN:9781510838819

Publisher

Curran Associates Inc.

Red Hook, NY, United States

Publication History

Published: 05 December 2016

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Cited By

You JWang YPal AEksombatchai PRosenburg CLeskovec J(2019)Hierarchical Temporal Convolutional Networks for Dynamic Recommender SystemsThe World Wide Web Conference10.1145/3308558.3313747(2236-2246)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313747
Song QChang SHu XTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Coupled Variational Recurrent Collaborative FilteringProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330940(335-343)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330940
Kumar SZhang XLeskovec JTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Predicting Dynamic Embedding Trajectory in Temporal Interaction NetworksProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330895(1269-1278)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330895
Xu HLuo DCarin L(2018)Online continuous-time tensor factorization based on pairwise interactive point processesProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304889.3305064(2905-2911)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304889.3305064
Liu YZhao LLiu GLu XGao PLi XJin Z(2018)Dynamic Bayesian logistic matrix factorization for recommendation with implicit feedbackProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304249(3463-3469)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304222.3304249
Wang YWilliams GTheodorou ESong L(2017)Variational policy for guiding point processesProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305890.3306062(3684-3693)Online publication date: 6-Aug-2017
https://dl.acm.org/doi/10.5555/3305890.3306062
Trivedi RDai HWang YSong L(2017)Know-evolveProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305890.3306039(3462-3471)Online publication date: 6-Aug-2017
https://dl.acm.org/doi/10.5555/3305890.3306039
Wang YYe XZha HSong L(2017)Predicting user activity level in point processes with mass transport equationProceedings of the 31st International Conference on Neural Information Processing Systems10.5555/3294771.3294928(1644-1654)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.5555/3294771.3294928
Dai HWang YTrivedi RSong LKaratzoglou AHidasi BTikk DSar-Shalom ORoitman HShapira BRokach L(2016)Recurrent Coevolutionary Latent Feature Processes for Continuous-Time RecommendationProceedings of the 1st Workshop on Deep Learning for Recommender Systems10.1145/2988450.2988451(29-34)Online publication date: 15-Sep-2016
https://dl.acm.org/doi/10.1145/2988450.2988451

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