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Large-scale Collaborative Ranking in Near-Linear Time

Published: 04 August 2017 Publication History

Abstract

In this paper, we consider the Collaborative Ranking (CR) problem for recommendation systems. Given a set of pairwise preferences between items for each user, collaborative ranking can be used to rank un-rated items for each user, and this ranking can be naturally used for recommendation. It is observed that collaborative ranking algorithms usually achieve better performance since they directly minimize the ranking loss; however, they are rarely used in practice due to the poor scalability. All the existing CR algorithms have time complexity at least O(|Ω|r) per iteration, where r is the target rank and |Ω| is number of pairs which grows quadratically with number of ratings per user. For example, the Netflix data contains totally 20 billion rating pairs, and at this scale all the current algorithms have to work with significant subsampling, resulting in poor prediction on testing data.
In this paper, we propose a new collaborative ranking algorithm called Primal-CR that reduces the time complexity to O(|Ω|+d1 |d2 r), where d1 is number of users and |d2 is the averaged number of items rated by a user. Note that d1 |d2 is strictly smaller and often much smaller than |Ω|.
Furthermore, by exploiting the fact that most data is in the form of numerical ratings instead of pairwise comparisons, we propose Primal-CR++ with O(d1|d2 (r+ log |d2)) time complexity. Both algorithms have better theoretical time complexity than existing approaches and also outperform existing approaches in terms of NDCG and pairwise error on real data sets. To the best of our knowledge, this is the first collaborative ranking algorithm capable of working on the full Netflix dataset using all the 20 billion rating pairs, and this leads to a model with much better recommendation compared with previous models trained on subsamples. Finally, compared with classical matrix factorization algorithm which also requires O(d1d2r) time, our algorithm has almost the same efficiency while making much better recommendations since we consider the ranking loss.

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Cited By

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  • (2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
  • (2021)Neural Collaborative Preference Learning With Pairwise ComparisonsIEEE Transactions on Multimedia10.1109/TMM.2020.300637323(1977-1989)Online publication date: 1-Jan-2021
  • (2020)SSE-PT: Sequential Recommendation Via Personalized TransformerProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412258(328-337)Online publication date: 22-Sep-2020
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cover image ACM Conferences
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2017
2240 pages
ISBN:9781450348874
DOI:10.1145/3097983
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: 04 August 2017

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Author Tags

  1. collaborative ranking
  2. large-scale
  3. recommendation systems

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2023)SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender SystemACM Transactions on Information Systems10.1145/362619442:2(1-32)Online publication date: 3-Oct-2023
  • (2021)Neural Collaborative Preference Learning With Pairwise ComparisonsIEEE Transactions on Multimedia10.1109/TMM.2020.300637323(1977-1989)Online publication date: 1-Jan-2021
  • (2020)SSE-PT: Sequential Recommendation Via Personalized TransformerProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412258(328-337)Online publication date: 22-Sep-2020
  • (2019)Stochastic shared embeddingsProceedings of the 33rd International Conference on Neural Information Processing Systems10.5555/3454287.3454290(24-34)Online publication date: 8-Dec-2019
  • (2019)Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent ArmsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.286604131:8(1569-1580)Online publication date: 1-Aug-2019
  • (2019)Joint Estimation of Trajectory and Dynamics from Paired Comparisons2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)10.1109/CAMSAP45676.2019.9022446(121-125)Online publication date: Dec-2019
  • (2019)Semi-supervised Classification-based Local Vertex Ranking via Dual Generative Adversarial Nets2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005595(1267-1273)Online publication date: Dec-2019

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