research-article

Dynamic Matrix Factorization with Priors on Unknown Values

Authors:

Robin Devooght,

Nicolas Kourtellis,

Amin MantrachAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 189 - 198

https://doi.org/10.1145/2783258.2783346

Published: 10 August 2015 Publication History

Abstract

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones not missing at random). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.

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Zhang PYan YZhang XKang LLi CHuang FWang SKim SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)GPT4Rec: Graph Prompt Tuning for Streaming RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657720(1774-1784)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657720
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Dynamic Matrix Factorization with Priors on Unknown Values

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cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
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Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

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Published: 10 August 2015

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Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

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

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https://dl.acm.org/doi/10.1145/3655632
Zhang PYan YZhang XKang LLi CHuang FWang SKim SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)GPT4Rec: Graph Prompt Tuning for Streaming RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657720(1774-1784)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657720
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https://doi.org/10.1016/j.neucom.2024.127338
He BHe XZhang RZhang YTang RMa CFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615135(741-750)Online publication date: 21-Oct-2023
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Chen CMa WZhang MWang CLiu YMa S(2023)Revisiting Negative Sampling vs. Non-sampling in Implicit RecommendationACM Transactions on Information Systems10.1145/352267241:1(1-25)Online publication date: 25-Feb-2023
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