research-article

The long tail of recommender systems and how to leverage it

Authors:

Yoon-Joo Park,

Alexander TuzhilinAuthors Info & Claims

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

Pages 11 - 18

https://doi.org/10.1145/1454008.1454012

Published: 23 October 2008 Publication History

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Abstract

The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.

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Cited By

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Alfaifi Y(2024)Recommender Systems Applications: Data Sources, Features, and ChallengesInformation10.3390/info1510066015:10(660)Online publication date: 21-Oct-2024
https://doi.org/10.3390/info15100660
Zhao LFu B(2024)Assessing the Impact of Recommendation Novelty on Older Consumers: Older Does Not Always Mean the Avoidance of Innovative ProductsBehavioral Sciences10.3390/bs1406047314:6(473)Online publication date: 5-Jun-2024
https://doi.org/10.3390/bs14060473
Li PTuzhilin A(2024)A Dynamical System Framework for Exploring Consumer Trajectories in Recommender SystemSSRN Electronic Journal10.2139/ssrn.4847168Online publication date: 2024
https://doi.org/10.2139/ssrn.4847168
Show More Cited By

Index Terms

The long tail of recommender systems and how to leverage it
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Information & Contributors

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Published In

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

October 2008

348 pages

ISBN:9781605580937

DOI:10.1145/1454008

General Chair:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
,
Program Chairs:
Derek Bridge
University College Cork, Ireland
,
Bamshad Mobasher
DePaul University, USA
,
Francesco Ricci
Free University Bozen-Bolzano, Italy

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2008

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RecSys08

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RecSys08: ACM Conference on Recommender Systems

October 23 - 25, 2008

Lausanne, Switzerland

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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Alfaifi Y(2024)Recommender Systems Applications: Data Sources, Features, and ChallengesInformation10.3390/info1510066015:10(660)Online publication date: 21-Oct-2024
https://doi.org/10.3390/info15100660
Zhao LFu B(2024)Assessing the Impact of Recommendation Novelty on Older Consumers: Older Does Not Always Mean the Avoidance of Innovative ProductsBehavioral Sciences10.3390/bs1406047314:6(473)Online publication date: 5-Jun-2024
https://doi.org/10.3390/bs14060473
Li PTuzhilin A(2024)A Dynamical System Framework for Exploring Consumer Trajectories in Recommender SystemSSRN Electronic Journal10.2139/ssrn.4847168Online publication date: 2024
https://doi.org/10.2139/ssrn.4847168
Besbes OKanoria YKumar A(2024)The Fault in Our Recommendations: On the Perils of Optimizing the MeasurableProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688144(200-208)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688144
Ungruh RDinnissen KVolk APera MHauptmann H(2024)Putting Popularity Bias Mitigation to the Test: A User-Centric Evaluation in Music RecommendersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688102(169-178)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688102
Wang YPiao HDong DYao QZhou JBaeza-Yates RBonchi F(2024)Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature InteractionsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671784(3233-3244)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671784
Zhong DWang XXu ZXu JWang WSerra ESpezzano F(2024)Interaction-level Membership Inference Attack against Recommender Systems with Long-tailed DistributionProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679804(3433-3442)Online publication date: 21-Oct-2024
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Petrov AMacdonald CAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)RecJPQ: Training Large-Catalogue Sequential RecommendersProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635821(538-547)Online publication date: 4-Mar-2024
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Chang BMeng CMa HChang SGu YPeng YFeng JZhang YBi SChi EChen MChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Cluster Anchor Regularization to Alleviate Popularity Bias in Recommender SystemsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648312(151-160)Online publication date: 13-May-2024
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Fang YWu HZhao YZhang LQin SWang X(2024)Diversifying Collaborative Filtering via Graph Spreading Network and Selective SamplingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.327247535:10(13860-13873)Online publication date: Oct-2024
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My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation

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