short-paper

Deriving Item Features Relevance from Past User Interactions

Authors:

Leonardo Cella,

Stefano Cereda,

Massimo Quadrana,

Paolo CremonesiAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 275 - 279

https://doi.org/10.1145/3079628.3079695

Published: 09 July 2017 Publication History

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Abstract

Item-based recommender systems suggest products based on the similarities between items computed either from past user preferences (collaborative filtering) or from item content features (content-based filtering). Collaborative filtering has been proven to outperform content-based filtering in a variety of scenarios. However, in item cold-start, collaborative filtering cannot be used directly since past user interactions are not available for the newly added items. Hence, content-based filtering is usually the only viable option left.

References

[1]

Asmaa Elbadrawy and George Karypis. 2015. User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items. ACM Trans. Intell. Syst. Technol. 6, 3, Article 33 (April 2015), 20 pages.

Digital Library

Google Scholar

[2]

Isabelle Guyon and André Elisseeff. 2003. An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3 (March 2003), 1157--1182. http://dl.acm.org/ citation.cfm?id=944919.944968

Digital Library

Google Scholar

[3]

Domonkos Tikk Istvn Pilszy. 2009. Recommending new movies: even a few ratings are more valuable than metadata. RecSys 09 Proceedings of the third ACM conference on Recommender systems (2009), 93 -- 100.

Digital Library

Google Scholar

[4]

Jon Herlocker Shilad Sen J. Ben Schafer, Dan Frankowski. 2007. Collaborative Filtering Recommender Systems. Lecture Notes in Computer Science (LNCS) 4321 (2007), 291, 324.

Crossref

Google Scholar

[5]

Marius Kaminskas and Derek Bridge. 2016. Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. ACM Trans. Interact. Intell. Syst. 7, 1, Article 2 (Dec. 2016), 42 pages.

Digital Library

Google Scholar

[6]

Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2011. Content-based recommender systems: State of the art and trends. In Recommender systems handbook. Springer, 73--105.

Google Scholar

[7]

Dunja Mladenic. July 1999. Text-Learning and Related Intelligent Agents: A Survey. IEEE Intelligent Systems 14 (July 1999), 44--54.

Digital Library

Google Scholar

[8]

Xia Ning and George Karypis. 2012. Sparse Linear Methods with Side Information for Top-N Recommendations. In Proceedings of the 21st International Conference on World Wide Web (WWW '12 Companion). ACM, New York, NY, USA, 581--582.

Digital Library

Google Scholar

[9]

Yannis Manolopoulos Panagiotis Symeonidis, Alexandros Nanopoulos. 2007. Feature-Weighted User Model for Recommender Systems. UM 07 Proceedings of the 11th international conference on User Modeling (2007), 97 -- 106.

Digital Library

Google Scholar

[10]

Steffen Rendle. 2012. Factorization Machines with libFM. ACM Trans. Intell. Syst. Technol. 3, 3, Article 57 (May 2012), 22 pages.

Digital Library

Google Scholar

[11]

George Karypis Xia Ning. 2011. SLIM: Sparse Linear Methods for Top-N Recommender Systems. IEEE.

Google Scholar

Cited By

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Bijay AAli JSobin C(2022)An Approach for Building Content Recommendation System for BilingualsProceedings of Second International Conference on Sustainable Expert Systems10.1007/978-981-16-7657-4_52(643-656)Online publication date: 26-Feb-2022
https://doi.org/10.1007/978-981-16-7657-4_52
Zhang CYao HYu LHuang CSong DChen HJiang MChawla N(2021)Inductive Contextual Relation Learning for PersonalizationACM Transactions on Information Systems10.1145/345035339:3(1-22)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3450353
Bernardis CCremonesi P(2021)NFC: a deep and hybrid item-based model for item cold-start recommendationUser Modeling and User-Adapted Interaction10.1007/s11257-021-09303-w32:4(747-780)Online publication date: 20-Oct-2021
https://doi.org/10.1007/s11257-021-09303-w
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Index Terms

Deriving Item Features Relevance from Past User Interactions
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
    2. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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Content-based recommendation systems can provide recommendations for "cold-start" items for which little or no training data is available, but typically have lower accuracy than collaborative filtering systems. Conversely, collaborative filtering ...

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Information

Published In

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

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: 09 July 2017

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Short-paper

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UMAP '17

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

Acceptance Rates

UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

Sponsor:
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33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

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Cited By

View all

Bijay AAli JSobin C(2022)An Approach for Building Content Recommendation System for BilingualsProceedings of Second International Conference on Sustainable Expert Systems10.1007/978-981-16-7657-4_52(643-656)Online publication date: 26-Feb-2022
https://doi.org/10.1007/978-981-16-7657-4_52
Zhang CYao HYu LHuang CSong DChen HJiang MChawla N(2021)Inductive Contextual Relation Learning for PersonalizationACM Transactions on Information Systems10.1145/345035339:3(1-22)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3450353
Bernardis CCremonesi P(2021)NFC: a deep and hybrid item-based model for item cold-start recommendationUser Modeling and User-Adapted Interaction10.1007/s11257-021-09303-w32:4(747-780)Online publication date: 20-Oct-2021
https://doi.org/10.1007/s11257-021-09303-w
AlRossais NKudenko DYuan T(2021)Improving cold-start recommendations using item-based stereotypesUser Modeling and User-Adapted Interaction10.1007/s11257-021-09293-931:5(867-905)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11257-021-09293-9
Deldjoo YFerrari Dacrema MConstantin MEghbal-Zadeh HCereda SSchedl MIonescu BCremonesi P(2019)Movie genomeUser Modeling and User-Adapted Interaction10.1007/s11257-019-09221-y29:2(291-343)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11257-019-09221-y
Cella LChampin PGandon FMédini LLalmas MIpeirotis P(2018)Efficient Context-Aware Sequential Recommender SystemCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191581(1391-1394)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3191581
Bianchi MCesaro FCiceri FDagrada MGasparin AGrattarola DInajjar IMetelli ACella L(2017)Content-Based approaches for Cold-Start Job RecommendationsProceedings of the Recommender Systems Challenge 201710.1145/3124791.3124793(1-5)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3124791.3124793

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Index Terms

Recommendations

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

Cross-representation mediation of user models

A unified approach to building hybrid recommender systems