short-paper

Top-N Recommendation with Missing Implicit Feedback

Authors:

Daryl Lim,

Julian McAuley,

Gert LanckrietAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 309 - 312

https://doi.org/10.1145/2792838.2799671

Published: 16 September 2015 Publication History

Get Access

Abstract

In implicit feedback datasets, non-interaction of a user with an item does not necessarily indicate that an item is irrelevant for the user. Thus, evaluation measures computed on the observed feedback may not accurately reflect performance on the complete data. In this paper, we discuss a missing data model for implicit feedback and propose a novel evaluation measure oriented towards Top-N recommendation. Our evaluation measure admits unbiased estimation under our missing data model, unlike the popular Normalized Discounted Cumulative Gain (NDCG) measure. We also derive an efficient algorithm to optimize the measure on the training data. We run several experiments which demonstrate the utility of our proposed measure.

References

[1]

Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proc. IEEE ICDM (2008), pages 263--272, 2008.

Digital Library

Google Scholar

[2]

Y. Kim and S. Choi. Bayesian binomial mixture model for collaborative prediction with non-random missing data. In RecSys '14, pages 201--208, 2014.

Digital Library

Google Scholar

[3]

Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009.

Digital Library

Google Scholar

[4]

D. Lim and G. Lanckriet. Efficient learning of mahalanobis metrics for ranking. In Proc. ICML 2014, pages 1980--1988, 2014.

Google Scholar

[5]

B. M. Marlin and R. S. Zemel. Collaborative prediction and ranking with non-random missing data. In RecSys '09, pages 5--12, 2009.

Digital Library

Google Scholar

[6]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. BPR: bayesian personalized ranking from implicit feedback. In UAI 2009, pages 452--461, 2009.

Digital Library

Google Scholar

[7]

Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, A. Hanjalic, and N. Oliver. Tfmap: optimizing map for top-n context-aware recommendation. In Proc. ACM SIGIR, 2012.

Digital Library

Google Scholar

[8]

H. Steck. Training and testing of recommender systems on data missing not at random. In Proc. ACM SIGKDD, pages 713--722, 2010.

Digital Library

Google Scholar

[9]

H. Steck. Evaluation of recommendations: rating-prediction and ranking. In RecSys '13, pages 213--220, 2013.

Digital Library

Google Scholar

[10]

J. Weston, S. Bengio, and N. Usunier. Large scale image annotation: learning to rank with joint word-image embeddings. Machine Learning, 81:21--35, 2010.

Digital Library

Google Scholar

Cited By

View all

Meng CZhao ZGuo WZhang YWu HGao CLi DLi XTang R(2023)Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/360636942:1(1-27)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1145/3606369
Li QWang XWang ZXu G(2023)Be Causal: De-Biasing Social Network Confounding in RecommendationACM Transactions on Knowledge Discovery from Data10.1145/353372517:1(1-23)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3533725
Zhang YSun FYang XXu COu WZhang Y(2020)Graph-based Regularization on Embedding Layers for RecommendationACM Transactions on Information Systems10.1145/341406739:1(1-27)Online publication date: 5-Sep-2020
https://dl.acm.org/doi/10.1145/3414067
Show More Cited By

Index Terms

Top-N Recommendation with Missing Implicit Feedback
1. Information systems
  1. Information retrieval

Recommendations

A latent pairwise preference learning approach for recommendation from implicit feedback
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Most of the current recommender systems heavily rely on explicit user feedback such as ratings on items to model users' interests. However, in many applications, it is very hard to collect the explicit feedback, while implicit feedback such as user ...
User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items
Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. ...
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01

Serendipitous recommendation has benefitted both e-retailers and users. It tends to suggest items which are both unexpected and useful to users. These items are not only profitable to the retailers but also surprisingly suitable to consumers' tastes. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, 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 the author(s) 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].

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

NSF
A*STAR

Conference

RecSys '15

Sponsor:

SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

Acceptance Rates

RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

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

Upcoming Conference

RecSys '24

Sponsor:
sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
643
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Meng CZhao ZGuo WZhang YWu HGao CLi DLi XTang R(2023)Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/360636942:1(1-27)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1145/3606369
Li QWang XWang ZXu G(2023)Be Causal: De-Biasing Social Network Confounding in RecommendationACM Transactions on Knowledge Discovery from Data10.1145/353372517:1(1-23)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3533725
Zhang YSun FYang XXu COu WZhang Y(2020)Graph-based Regularization on Embedding Layers for RecommendationACM Transactions on Information Systems10.1145/341406739:1(1-27)Online publication date: 5-Sep-2020
https://dl.acm.org/doi/10.1145/3414067
Tian MEkstrand MO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)Estimating Error and Bias in Offline Evaluation ResultsProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3378004(392-396)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3378004
Carraro DBridge DHung CCerny TShin DBechini A(2020)Debiased offline evaluation of recommender systemsProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3375759(1435-1442)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3375759
Zhang YXu XZhou HZhang YCaverlee JHu XLalmas MWang W(2020)Distilling Structured Knowledge into Embeddings for Explainable and Accurate RecommendationProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371790(735-743)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371790
Zhu ZWang JCaverlee J(2019)Improving Top-K Recommendation via JointCollaborative AutoencodersThe World Wide Web Conference10.1145/3308558.3313678(3483-3482)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313678
Wei TLi Y(2019)Does Tail Label Help for Large-Scale Multi-Label Learning?IEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2019.2935143(1-10)Online publication date: 2019
https://doi.org/10.1109/TNNLS.2019.2935143
Wei TLi Y(2018)Does tail label help for large-scale multi-label learningProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304889.3305056(2847-2853)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304889.3305056
Yang LCui YXuan YWang CBelongie SEstrin DPera SEkstrand MAmatriain XO'Donovan J(2018)Unbiased offline recommender evaluation for missing-not-at-random implicit feedbackProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240355(279-287)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3240355
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

A latent pairwise preference learning approach for recommendation from implicit feedback

User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items

Serendipitous Personalized Ranking for Top-N Recommendation