skip to main content
10.1145/2959100.2959186acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
research-article

Recommendations with a Purpose

Published: 07 September 2016 Publication History

Abstract

The purpose of recommenders is often summarized as "help the users find relevant items", and the predominant operationalization of this goal has been to focus on the ability to numerically estimate the users' preferences for unseen items or to provide users with item lists ranked in accordance to the estimated preferences. This dominant, albeit narrow, view of the recommendation problem has been tremendously helpful in advancing research in different ways, e.g., through the establishment of standardized evaluation procedures and metrics. In reality, recommender systems can serve a variety of purposes from the point of view of both consumers and providers. Most of the purposes, however, are significantly underexplored, even though many of them are arguably more aligned with the real-world expectations for recommenders than our current predominant paradigm. Therefore, it is important to revisit our conceptualizations of the potential goals of recommenders and their operationalization as research problems. In this paper, we discuss a framework of recommendation goals and purposes and highlight possible future directions and challenges related to the operationalization of such alternative problem formulations.

References

[1]
G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 17(6):734--749, 2005.
[2]
F. Garcin, B. Faltings, O. Donatsch, A. Alazzawi, C. Bruttin, and A. Huber. Offline and online evaluation of news recommender systems at swissinfo.ch. In RecSys '14, pages 169--176, 2014.
[3]
C. A. Gomez-Uribe and N. Hunt. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM TMIS, 6(4):13:1--13:19, 2015.
[4]
J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating Collaborative Filtering Recommender Systems. ACM TOIS, 22(1):5--53, 2004.
[5]
D. Jannach and K. Hegelich. A case study on the effectiveness of recommendations in the mobile internet. In RecSys '09, pages 205--208, 2009.

Cited By

View all
  • (2024)Embedding Democratic Values into Social Media AIs via Societal Objective FunctionsProceedings of the ACM on Human-Computer Interaction10.1145/36410028:CSCW1(1-36)Online publication date: 26-Apr-2024
  • (2024)It's (not) all about that CTR: A Multi-Stakeholder Perspective on News Recommender MetricsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688183(999-1003)Online publication date: 8-Oct-2024
  • (2024)How to Evaluate Serendipity in Recommender Systems: the Need for a SerendiptionnaireProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688017(1335-1341)Online publication date: 8-Oct-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems
September 2016
490 pages
ISBN:9781450340359
DOI:10.1145/2959100
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].

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. foundations of recommender systems
  2. recommendation goals and purposes

Qualifiers

  • Research-article

Conference

RecSys '16
Sponsor:
RecSys '16: Tenth ACM Conference on Recommender Systems
September 15 - 19, 2016
Massachusetts, Boston, USA

Acceptance Rates

RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)175
  • Downloads (Last 6 weeks)14
Reflects downloads up to 01 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Embedding Democratic Values into Social Media AIs via Societal Objective FunctionsProceedings of the ACM on Human-Computer Interaction10.1145/36410028:CSCW1(1-36)Online publication date: 26-Apr-2024
  • (2024)It's (not) all about that CTR: A Multi-Stakeholder Perspective on News Recommender MetricsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688183(999-1003)Online publication date: 8-Oct-2024
  • (2024)How to Evaluate Serendipity in Recommender Systems: the Need for a SerendiptionnaireProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688017(1335-1341)Online publication date: 8-Oct-2024
  • (2024)Product Recommendation System Using Large Language Model: Llama-22024 IEEE World AI IoT Congress (AIIoT)10.1109/AIIoT61789.2024.10579009(0491-0495)Online publication date: 29-May-2024
  • (2024)Model-based approaches to profit-aware recommendationExpert Systems with Applications10.1016/j.eswa.2024.123642249(123642)Online publication date: Sep-2024
  • (2024)Examining the merits of feature-specific similarity functions in the news domain using human judgmentsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09412-234:4(995-1042)Online publication date: 7-Aug-2024
  • (2024)Engineering recommender systems for modelling languages: concept, tool and evaluationEmpirical Software Engineering10.1007/s10664-024-10483-329:4Online publication date: 18-Jun-2024
  • (2024)The Role of Human-Centered AI in User Modeling, Adaptation, and Personalization—Models, Frameworks, and ParadigmsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_2(43-84)Online publication date: 1-May-2024
  • (2023)The AI Learns to Lie to Please You: Preventing Biased Feedback Loops in Machine-Assisted Intelligence AnalysisAnalytics10.3390/analytics20200202:2(350-358)Online publication date: 18-Apr-2023
  • (2023)A survey on multi-objective recommender systemsFrontiers in Big Data10.3389/fdata.2023.11578996Online publication date: 22-Mar-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media