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Recommender Systems with Personality

Authors:

Jason HongAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 207 - 210

https://doi.org/10.1145/2959100.2959138

Published: 07 September 2016 Publication History

Abstract

We believe that in the future, the most common form of recommender systems will be present in a personal assistant. We claim that such an intelligent agent must be personal, i.e., know its user's preferences and recommend relevant content, a dynamic learner, instructable, supportive and affable. We describe the current state of the art and the challenges which should be addressed in each of these agent properties and provide examples of how we expect future personal agents to convey these properties.

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Cited By

Tricomi GD'Agati LBenomar ZLongo FMerlino GPuliafito A(2022)Interfacing Intelligent Personal Assistant to SDI/O with one click2022 International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN54977.2022.9868902(1-8)Online publication date: Jul-2022
https://doi.org/10.1109/ICCCN54977.2022.9868902
Chkroun MAzaria A(2021)A Safe Collaborative Chatbot for Smart Home AssistantsSensors10.3390/s2119664121:19(6641)Online publication date: 6-Oct-2021
https://doi.org/10.3390/s21196641
Vuong TAndolina SJacucci GRuotsalo T(2021)Spoken Conversational Context Improves Query Auto-completion in Web SearchACM Transactions on Information Systems10.1145/344787539:3(1-32)Online publication date: 5-May-2021
https://dl.acm.org/doi/10.1145/3447875
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Index Terms

Recommender Systems with Personality

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EMPIRE '15: Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015

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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

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

Copyright © 2016 ACM.

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].

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2016

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Yahoo!

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RecSys '16

Sponsor:

SIGCHI

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%

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Cited By

Tricomi GD'Agati LBenomar ZLongo FMerlino GPuliafito A(2022)Interfacing Intelligent Personal Assistant to SDI/O with one click2022 International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN54977.2022.9868902(1-8)Online publication date: Jul-2022
https://doi.org/10.1109/ICCCN54977.2022.9868902
Chkroun MAzaria A(2021)A Safe Collaborative Chatbot for Smart Home AssistantsSensors10.3390/s2119664121:19(6641)Online publication date: 6-Oct-2021
https://doi.org/10.3390/s21196641
Vuong TAndolina SJacucci GRuotsalo T(2021)Spoken Conversational Context Improves Query Auto-completion in Web SearchACM Transactions on Information Systems10.1145/344787539:3(1-32)Online publication date: 5-May-2021
https://dl.acm.org/doi/10.1145/3447875
Abduljabbar TTao XZhang JZhou XLi LCai Y(2021)A Survey of Privacy Solutions using Blockchain for Recommender Systems: Current Status, Classification and Open IssuesThe Computer Journal10.1093/comjnl/bxab065Online publication date: 31-May-2021
https://doi.org/10.1093/comjnl/bxab065
Wang HZuo YLi HWu J(2021)Cross-domain recommendation with user personalityKnowledge-Based Systems10.1016/j.knosys.2020.106664213(106664)Online publication date: Feb-2021
https://doi.org/10.1016/j.knosys.2020.106664
Yakhchi SGhafari SOrgun M(2021)TAP: A Two-Level Trust and Personality-Aware Recommender SystemService-Oriented Computing – ICSOC 2020 Workshops10.1007/978-3-030-76352-7_30(294-308)Online publication date: 30-May-2021
https://doi.org/10.1007/978-3-030-76352-7_30
Safovich YAzaria A(2020)Fiction Sentence Expansion and Enhancement via Focused Objective and Novelty Curve Sampling2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00132(835-843)Online publication date: Nov-2020
https://doi.org/10.1109/ICTAI50040.2020.00132
de Barcelos Silva AGomes Mda Costa Cda Rosa Righi RBarbosa JPessin GDe Doncker GFederizzi G(2020)Intelligent personal assistantsExpert Systems with Applications: An International Journal10.1016/j.eswa.2020.113193147:COnline publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1016/j.eswa.2020.113193
Azaria ANivasch KElkind EVeloso MAgmon NTaylor M(2019)The Multimodal Correction Detection ProblemProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331918(1784-1786)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331918
Pecune FMurali STsai VMatsuyama YCassell JOka NKoda TObaid MNakanishi HMubin OTanaka K(2019)A Model of Social Explanations for a Conversational Movie Recommendation SystemProceedings of the 7th International Conference on Human-Agent Interaction10.1145/3349537.3351899(135-143)Online publication date: 25-Sep-2019
https://dl.acm.org/doi/10.1145/3349537.3351899
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