short-paper

Analysis of cold-start recommendations in IPTV systems

Authors:

Paolo Cremonesi,

Roberto TurrinAuthors Info & Claims

RecSys '09: Proceedings of the third ACM conference on Recommender systems

Pages 233 - 236

https://doi.org/10.1145/1639714.1639756

Published: 23 October 2009 Publication History

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Abstract

In this paper we evaluate the performance of different collaborative algorithms in cold-start situations, where the initial lack of ratings may affect the quality of the algorithms. The evaluation has been performed on the pay-per-view datasets collected by two IP-television providers over a period of several months. The analysis shows that item-based algorithms perform better with respect to SVD-based ones in the early stage of the cold-start problem. Moreover, the accuracy of SVD-based algorithms, when using few latent factors, decreases with the time-evolution of the dataset. On the contrary, the same algorithms used with a large-enough number of latent features increase their accuracy with time and may outperform the item-based algorithms.

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Chen JWu H(2022)Correlation Analysis of Population Educational Structure and Program Audience Share Based on Multisample Regression for CorrectionSecurity and Communication Networks10.1155/2022/54378162022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5437816
Hlupic TOrescanin DBaranovic M(2022)A Novel Method for IPTV Customer Behavior Analysis Using Time SeriesIEEE Access10.1109/ACCESS.2022.316440910(37003-37015)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3164409
Gaspar PKompan MKoncal MBielikova M(2019)Improving the Personalized Recommendation in the Cold-start Scenarios2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2019.00079(606-607)Online publication date: Oct-2019
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Index Terms

Analysis of cold-start recommendations in IPTV systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

RecSys '09: Proceedings of the third ACM conference on Recommender systems

October 2009

442 pages

ISBN:9781605584355

DOI:10.1145/1639714

General Chairs:
Lawrence Bergman
IBM Research, NY, USA
,
Alex Tuzhilin
New York University, NY, USA
,
Program Chairs:
Robin Burke
DePaul University, USA
,
Alexander Felfernig
Graz University of Technology, Austria
,
Lars Schmidt-Thieme
University of Hildesheim, Germany

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 2009

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RecSys '09

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SIGCHI

RecSys '09: Third ACM Conference on Recommender Systems

October 23 - 25, 2009

New York, New York, USA

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

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Cited By

View all

Chen JWu H(2022)Correlation Analysis of Population Educational Structure and Program Audience Share Based on Multisample Regression for CorrectionSecurity and Communication Networks10.1155/2022/54378162022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5437816
Hlupic TOrescanin DBaranovic M(2022)A Novel Method for IPTV Customer Behavior Analysis Using Time SeriesIEEE Access10.1109/ACCESS.2022.316440910(37003-37015)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3164409
Gaspar PKompan MKoncal MBielikova M(2019)Improving the Personalized Recommendation in the Cold-start Scenarios2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2019.00079(606-607)Online publication date: Oct-2019
https://doi.org/10.1109/DSAA.2019.00079
Dixit VGupta SJain P(2018)A Propound Hybrid Approach for Personalized Online Product RecommendationsApplied Artificial Intelligence10.1080/08839514.2018.150877332:9-10(785-801)Online publication date: 17-Aug-2018
https://doi.org/10.1080/08839514.2018.1508773
Abdollahpouri AQavami RMoradi P(2018)On the synthetic dataset generation for IPTV services based on user behaviorMultimedia Tools and Applications10.1007/s11042-017-4746-277:7(8475-8493)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s11042-017-4746-2
Ferdaous HBouchra FBrahim OImad-Eddine MAsmaa B(2018)Recommendation using a clustering algorithm based on a hybrid features selection methodJournal of Intelligent Information Systems10.1007/s10844-017-0493-051:1(183-205)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10844-017-0493-0
Sürer ÖCremonesi PRicci FBerkovsky STuzhilin A(2017)Improving Similarity Measures Using Ontological DataProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109863(416-420)Online publication date: 27-Aug-2017
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Chekkai NTahraoui MHamadouche MChikhi SKheddouci HMeshoul SBouaziz A(2017)CSCF: Clustering based-approach for social collaborative filtering2017 First International Conference on Embedded & Distributed Systems (EDiS)10.1109/EDIS.2017.8284027(1-6)Online publication date: Dec-2017
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Christou IAmolochitis ETan Z(2016)AMOREKnowledge and Information Systems10.1007/s10115-015-0866-z47:3(671-696)Online publication date: 1-Jun-2016
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