poster

Intent-oriented diversity in recommender systems

Authors:

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 1211 - 1212

https://doi.org/10.1145/2009916.2010124

Published: 24 July 2011 Publication History

Get Access

Abstract

Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from ad-hoc IR to the recommendation task, by introducing the notion of user profile aspect as an analogue of query intent. As a particular approach, user aspects are automatically extracted from latent item features. Empirical results support the proposed approach and provide further insights.

References

[1]

Agrawal, A., Gollapudi, S., Halverson, A., and Ieong, S. Diversifying search results. WSDM 2009. Barcelona, Spain, 2009, 5--14.

Digital Library

Google Scholar

[2]

Carbonell, J. G., and Goldstein, J. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. SIGIR 1998. Melbourne, Australia, 1998, 335--336.

Digital Library

Google Scholar

[3]

Clarke, C. L. A. et al. Novelty and diversity in information retrieval evaluation. SIGIR 2008. Singapore, July 2008, 659--666.

Digital Library

Google Scholar

[4]

Koren, Y., Bell, R. M, Volinsky, C. Matrix Factorization Techniques for Recommender Systems. IEEE Computer 42(8):30--37, 2009.

Digital Library

Google Scholar

[5]

Zhang, M. and Hurley, N. Avoiding Monotony: Improving the Diversity of Recommendation Lists. RecSys 2008. Lausanne, Switzerland, October 2008, 123--130.

Digital Library

Google Scholar

[6]

Ziegler, C-N., McNee, S. M., Konstan, J. A., Lausen, G. Improving recommendation lists through topic diversification. WWW 2005. Chiba, Japan, May 2005, 22--32.

Digital Library

Google Scholar

Cited By

View all

Jannach DZanker M(2024)A Survey on Intent-aware Recommender SystemsACM Transactions on Recommender Systems10.1145/37008903:2(1-32)Online publication date: 16-Oct-2024
https://dl.acm.org/doi/10.1145/3700890
Zhao YWang YLiu YCheng XAggarwal CDerr T(2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/366492816:1(1-28)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3664928
Bai YZhou YDou ZWen J(2024)Intent-Oriented Dynamic Interest Modeling for Personalized Web SearchACM Transactions on Information Systems10.1145/363981742:4(1-30)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3639817
Show More Cited By

Index Terms

Intent-oriented diversity in recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Temporal diversity in recommender systems
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items ...
Injecting Semantic Diversity in Top-N Recommender Systems Using Determinantal Point Processes and Curated Lists
UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

Top-N Recommender Systems usually suffer from intra-list diversity as they are tailored for relevance and predicted rating accuracy. This problem is magnified in the case of cold start setting - resulting in users being restricted to popular set of ...
A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Much of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the ...

Comments

Information & Contributors

Information

Published In

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

SIGIR '11

Sponsor:

SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

49
Total Citations
View Citations
675
Total Downloads

Downloads (Last 12 months)59
Downloads (Last 6 weeks)1

Reflects downloads up to 24 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Jannach DZanker M(2024)A Survey on Intent-aware Recommender SystemsACM Transactions on Recommender Systems10.1145/37008903:2(1-32)Online publication date: 16-Oct-2024
https://dl.acm.org/doi/10.1145/3700890
Zhao YWang YLiu YCheng XAggarwal CDerr T(2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/366492816:1(1-28)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3664928
Bai YZhou YDou ZWen J(2024)Intent-Oriented Dynamic Interest Modeling for Personalized Web SearchACM Transactions on Information Systems10.1145/363981742:4(1-30)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3639817
Rook LZanker MJannach DBoratto LGena CMarras MGermanakos PPopescus E(2024)Are We Losing Interest in Context-Aware Recommender Systems?Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665190(229-230)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665190
Wu HZhang YMa CLyu FHe BMitra BLiu X(2024)Result Diversification in Search and Recommendation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338226236:10(5354-5373)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2024.3382262
Satinet CFouss FSaerens MLeleux P(2024)In-processing and post-processing strategies for balancing accuracy and sustainability in product recommendationsElectronic Commerce Research and Applications10.1016/j.elerap.2024.10143367:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.elerap.2024.101433
Yadav NPal SSingh A(2023)Diversified recommendation using implicit content node embedding in heterogeneous information networkMultimedia Tools and Applications10.1007/s11042-023-16135-w83:7(20605-20635)Online publication date: 3-Aug-2023
https://doi.org/10.1007/s11042-023-16135-w
Rafique WHafid AQadir J(2023)Developing smart city services using intent‐aware recommendation systems: A surveyTransactions on Emerging Telecommunications Technologies10.1002/ett.472834:4Online publication date: 12-Jan-2023
https://doi.org/10.1002/ett.4728
Zhang XJiang MChen HChen CZheng J(2022)Cloze-Style Data Augmentation for Few-Shot Intent RecognitionMathematics10.3390/math1018335810:18(3358)Online publication date: 16-Sep-2022
https://doi.org/10.3390/math10183358
Karakolis EKokkinakos PAskounis D(2022)Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender SystemsApplied Sciences10.3390/app1210498412:10(4984)Online publication date: 14-May-2022
https://doi.org/10.3390/app12104984
Show More Cited By

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Temporal diversity in recommender systems

Injecting Semantic Diversity in Top-N Recommender Systems Using Determinantal Point Processes and Curated Lists

A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations