poster

A probabilistic definition of item similarity

Authors:

Niranjan BhosarekarAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 229 - 236

https://doi.org/10.1145/2043932.2043973

Published: 23 October 2011 Publication History

Abstract

In item-based collaborative filtering, a critical intermediate step to personalized recommendations is the definition of an item-similarity metric. Existing algorithms compute the item-similarity using the user-to-item ratings (cosine, Pearson, Jaccard, etc.). When computing the similarity between two items A and B many of these algorithms divide the actual number of co-occurring users by some "difficulty" of co-occurrence. We refine this approach by defining item similarity as the ratio of the actual number of co-occurrences to the number of co-occurrences that would be expected if user choices were random. In the final step of our method to compute personalized recommendations we apply the usage history of a user to the item similarity matrix. The well defined probabilistic meaning of our similarities allows us to further improve this final step. We measured the quality of our algorithm on a large real-world data-set. As part of Comcast's efforts to improve its personalized recommendations of movies and TV shows, several top recommender companies were invited to apply their algorithms to one year of Video-on-Demand usage data. Our algorithm tied for first place. This paper includes a MapReduce pseudo code implementation of our algorithm.

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Cited By

Rome SHamidian SWalsh RFoley KTure FAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Learning to Rank Instant Search Results with Multiple IndicesProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3536334(3412-3416)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3536334
Wei-Jin JJia-Hui CYu-Hui X(2018)Intelligent prediction and recommendation optimization method based on fuzzy clustering and time weightingProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282394(79-84)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282394
Berbague CKarabadji NSeridi H(2018)Recommendation diversification using a weighted similarity measure in user based collaborative filtering2018 International Symposium on Programming and Systems (ISPS)10.1109/ISPS.2018.8379011(1-6)Online publication date: Apr-2018
https://doi.org/10.1109/ISPS.2018.8379011
Show More Cited By

Index Terms

A probabilistic definition of item similarity
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

cover image ACM Conferences

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

Copyright © 2011 ACM.

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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Publication History

Published: 23 October 2011

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SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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

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Cited By

Rome SHamidian SWalsh RFoley KTure FAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Learning to Rank Instant Search Results with Multiple IndicesProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3536334(3412-3416)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3536334
Wei-Jin JJia-Hui CYu-Hui X(2018)Intelligent prediction and recommendation optimization method based on fuzzy clustering and time weightingProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282394(79-84)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282394
Berbague CKarabadji NSeridi H(2018)Recommendation diversification using a weighted similarity measure in user based collaborative filtering2018 International Symposium on Programming and Systems (ISPS)10.1109/ISPS.2018.8379011(1-6)Online publication date: Apr-2018
https://doi.org/10.1109/ISPS.2018.8379011
Feng WZhu QZhuang JYu S(2018)An expert recommendation algorithm based on Pearson correlation coefficient and FP-growthCluster Computing10.1007/s10586-017-1576-y22:S3(7401-7412)Online publication date: 3-Jan-2018
https://doi.org/10.1007/s10586-017-1576-y
Zhang ZHu LQiu MGao F(2016)Events detection and community partition based on probabilistic snapshot for evolutionary social networkPeer-to-Peer Networking and Applications10.1007/s12083-016-0427-610:6(1272-1284)Online publication date: 1-Feb-2016
https://doi.org/10.1007/s12083-016-0427-6

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