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Optimizing top-n collaborative filtering via dynamic negative item sampling

Published: 28 July 2013 Publication History

Abstract

Collaborative filtering techniques rely on aggregated user preference data to make personalized predictions. In many cases, users are reluctant to explicitly express their preferences and many recommender systems have to infer them from implicit user behaviors, such as clicking a link in a webpage or playing a music track. The clicks and the plays are good for indicating the items a user liked (i.e., positive training examples), but the items a user did not like (negative training examples) are not directly observed. Previous approaches either randomly pick negative training samples from unseen items or incorporate some heuristics into the learning model, leading to a biased solution and a prolonged training period. In this paper, we propose to dynamically choose negative training samples from the ranked list produced by the current prediction model and iteratively update our model. The experiments conducted on three large-scale datasets show that our approach not only reduces the training time, but also leads to significant performance gains.

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  • (2025)Joint utilization of positive and negative pseudo-labels in semi-supervised facial expression recognitionPattern Recognition10.1016/j.patcog.2024.111147159(111147)Online publication date: Mar-2025
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  • (2024)Learning-efficient yet generalizable collaborative filtering for item recommendationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693743(41183-41203)Online publication date: 21-Jul-2024
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  1. Optimizing top-n collaborative filtering via dynamic negative item sampling

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      cover image ACM Conferences
      SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
      July 2013
      1188 pages
      ISBN:9781450320344
      DOI:10.1145/2484028
      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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      Published: 28 July 2013

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      Author Tags

      1. negative item sampling
      2. ranking-oriented collaborative filtering
      3. recommender systems

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      SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;
      Overall Acceptance Rate 792 of 3,983 submissions, 20%

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      • (2025)Self-supervised contrastive learning for implicit collaborative filteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109563139:PAOnline publication date: 1-Jan-2025
      • (2024)Learning-efficient yet generalizable collaborative filtering for item recommendationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693743(41183-41203)Online publication date: 21-Jul-2024
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