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Interactive Storytelling for Movie Recommendation through Latent Semantic Analysis

Authors:

Wencheng WangAuthors Info & Claims

IUI '18: Proceedings of the 23rd International Conference on Intelligent User Interfaces

Pages 521 - 533

https://doi.org/10.1145/3172944.3172979

Published: 05 March 2018 Publication History

Abstract

Recommendation is essential to many online services; however current systems often provide limited interaction and visualization mechanisms, affecting the user satisfaction of recommendation. This paper presents an interactive recommendation approach for the general public without any knowledge of recommendation or visualization algorithms. Our approach emphasizes interactivity, explicit user input, and semantic information convey with the following two components. First, we propose a Latent Semantic Model that captures the statistical features of semantic concepts on 2D domains and abstracts user preferences for personal recommendation, so that high-dimensional spectral space from the rating records can be understood and interacted with directly. Second, we propose an interactive recommendation approach through a storytelling mechanism for promoting the communication between the user and the recommendation system. We demonstrate and evaluate our approach with a real dataset. Our approach can also be extended to other applications including various online recommendation systems.

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Cited By

Wu JWang YDai HXu CKent K(2023)Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion DetectionIEEE Internet of Things Journal10.1109/JIOT.2023.326245810:15(13205-13220)Online publication date: 1-Aug-2023
https://doi.org/10.1109/JIOT.2023.3262458
Ang GLim EHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)Learning Network-Based Multi-Modal Mobile User Interface EmbeddingsProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450693(366-376)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450693
Suleman RKorkontzelos I(2021)Extending latent semantic analysis to manage its syntactic blindnessExpert Systems with Applications10.1016/j.eswa.2020.114130165(114130)Online publication date: Mar-2021
https://doi.org/10.1016/j.eswa.2020.114130
Show More Cited By

Index Terms

Interactive Storytelling for Movie Recommendation through Latent Semantic Analysis
1. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods
      1. User centered design
  2. Visualization
    1. Visualization application domains
      1. Information visualization

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cover image ACM Conferences

IUI '18: Proceedings of the 23rd International Conference on Intelligent User Interfaces

March 2018

698 pages

ISBN:9781450349451

DOI:10.1145/3172944

General Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Yoshinori Hijikata
Kwansei Gakuin University, Japan
,
Jun Rekimoto
University of Tokyo, Japan
,
Program Chairs:
Margaret Burnett
Oregon State University, USA
,
Mark Billinghurst
University of South Australia, Australia
,
Aaron Quigley
University of St Andrews, UK

Copyright © 2018 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 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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SIGAI: ACM Special Interest Group on Artificial Intelligence

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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

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Published: 05 March 2018

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NSF
National Natural Science Foundation of China

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IUI'18

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SIGAI

IUI'18: 23rd International Conference on Intelligent User Interfaces

March 7 - 11, 2018

Tokyo, Japan

Acceptance Rates

IUI '18 Paper Acceptance Rate 43 of 299 submissions, 14%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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IUI '25

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sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

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Cited By

Wu JWang YDai HXu CKent K(2023)Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion DetectionIEEE Internet of Things Journal10.1109/JIOT.2023.326245810:15(13205-13220)Online publication date: 1-Aug-2023
https://doi.org/10.1109/JIOT.2023.3262458
Ang GLim EHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)Learning Network-Based Multi-Modal Mobile User Interface EmbeddingsProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450693(366-376)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450693
Suleman RKorkontzelos I(2021)Extending latent semantic analysis to manage its syntactic blindnessExpert Systems with Applications10.1016/j.eswa.2020.114130165(114130)Online publication date: Mar-2021
https://doi.org/10.1016/j.eswa.2020.114130
Duan LGao TNi WWang W(2021)A hybrid intelligent service recommendation by latent semantics and explicit ratingsInternational Journal of Intelligent Systems10.1002/int.2261236:12(7867-7894)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1002/int.22612
Kleemann TZiegler JPreim BNürnberger AHansen C(2020)Distribution slidersProceedings of Mensch und Computer 202010.1145/3404983.3405512(67-78)Online publication date: 6-Sep-2020
https://dl.acm.org/doi/10.1145/3404983.3405512
Xu KOttley AWalchshofer CStreit MChang RWenskovitch J(2020)Survey on the Analysis of User Interactions and Visualization ProvenanceComputer Graphics Forum10.1111/cgf.1403539:3(757-783)Online publication date: 18-Jul-2020
https://doi.org/10.1111/cgf.14035
Sun JGeng JCheng XZhu MXu QLiu Y(2020)Leveraging personality information to improve community recommendation in e‐learning platformsBritish Journal of Educational Technology10.1111/bjet.1301151:5(1711-1733)Online publication date: 6-Aug-2020
https://doi.org/10.1111/bjet.13011

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