short-paper

Beyond rating prediction accuracy: on new perspectives in recommender systems

Author:

Panagiotis AdamopoulosAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 459 - 462

https://doi.org/10.1145/2507157.2508073

Published: 12 October 2013 Publication History

Abstract

This paper proposes a number of studies in order to move recommender systems beyond the traditional paradigm and the classical perspective of rating prediction accuracy. We contribute to existing helpful but less explored paradigms and also propose new approaches aiming at more useful recommendations for both users and businesses. Working toward this direction, we discuss the studies we have conducted so far and present our future research plans. In particular, we move our focus from even more accurate rating predictions and aim at offering a holistic experience to the users by avoiding the over-specialization of generated recommendations and providing the users with sets of non-obvious but high quality recommendations that fairly match their interests and they will remarkably like.

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Cited By

Adamopoulos PTodri V(2024)Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation AppProduction and Operations Management10.1177/10591478241259422Online publication date: 25-Jul-2024
https://doi.org/10.1177/10591478241259422
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Singh RSingh PSingh JSingh ADhanasekaran S(2022)Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation SystemApplied Sciences10.3390/app12221168612:22(11686)Online publication date: 17-Nov-2022
https://doi.org/10.3390/app122211686
Show More Cited By

Index Terms

Beyond rating prediction accuracy: on new perspectives in recommender systems
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

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Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

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RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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Cited By

Adamopoulos PTodri V(2024)Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation AppProduction and Operations Management10.1177/10591478241259422Online publication date: 25-Jul-2024
https://doi.org/10.1177/10591478241259422
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Singh RSingh PSingh JSingh ADhanasekaran S(2022)Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation SystemApplied Sciences10.3390/app12221168612:22(11686)Online publication date: 17-Nov-2022
https://doi.org/10.3390/app122211686
Kokkodis MIpeirotis P(2021)Demand-Aware Career Path RecommendationsManagement Science10.1287/mnsc.2020.372767:7(4362-4383)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1287/mnsc.2020.3727
Kokkodis M(2021)Dynamic, Multidimensional, and Skillset-Specific Reputation Systems for Online WorkInformation Systems Research10.1287/isre.2020.097232:3(688-712)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1287/isre.2020.0972
Adamopoulos PTodri VGhose A(2021)Demand Effects of the Internet-of-Things Sales Channel: Evidence from Automating the Purchase ProcessInformation Systems Research10.1287/isre.2020.096232:1(238-267)Online publication date: 1-Mar-2021
https://doi.org/10.1287/isre.2020.0962
Singal SSingh PDahiya M(2021)Automatic Collection Creation and RecommendationProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478865(633-638)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3478865
Singh PSinha MDas SChoudhury P(2020)Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar itemApplied Intelligence10.1007/s10489-020-01775-4Online publication date: 2-Aug-2020
https://doi.org/10.1007/s10489-020-01775-4
Gal DSimonson I(2020)Predicting consumers’ choices in the age of the internet, AI, and almost perfect tracking: Some things change, the key challenges do notConsumer Psychology Review10.1002/arcp.10684:1(135-152)Online publication date: 9-Dec-2020
https://doi.org/10.1002/arcp.1068
Gogna AMajumdar A(2017)DiABlOInformation Sciences: an International Journal10.1016/j.ins.2016.10.043378:C(59-74)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.ins.2016.10.043
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