Article

MovieLens unplugged: experiences with an occasionally connected recommender system

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John RiedlAuthors Info & Claims

IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces

Pages 263 - 266

https://doi.org/10.1145/604045.604094

Published: 12 January 2003 Publication History

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Abstract

Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our experience with implementing a recommender system on a PDA that is occasionally connected to the network. This interface helps users of the MovieLens movie recommendation service select movies to rent, buy, or see while away from their computer. The results of a nine month field study show that although there are several challenges to overcome, mobile recommender systems have the potential to provide value to their users today

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Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
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Index Terms

MovieLens unplugged: experiences with an occasionally connected recommender system
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces

January 2003

344 pages

ISBN:1581135866

DOI:10.1145/604045

Conference Chair:
David Leake
Indiana University
,
Program Chairs:
Lewis Johnson
USC/Information Sciences Institute
,
Elisabeth Andre
University of Augsburg

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: 12 January 2003

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IUI03: Eighth International Conference on Intelligent User Interfaces

January 12 - 15, 2003

Florida, Miami, USA

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

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Yong JKim C(2024)Hybrid Inductive Graph Method for Matrix CompletionInternational Journal of Data Warehousing and Mining10.4018/IJDWM.34536120:1(1-16)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.4018/IJDWM.345361
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Sun JChen JDing GLin FSong Y(2024)Spectrum Recommendation in Cognitive Internet of Things: A Knowledge-Graph-Based FrameworkIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2023.331359110:1(21-34)Online publication date: Feb-2024
https://doi.org/10.1109/TCCN.2023.3313591
Karthiyayini JAnandhi R(2024)To Analyze the Various Machine Learning Algorithms That Can Effectively Process Large Volumes of Data and Extract Relevant Information for Personalized Travel RecommendationsSN Computer Science10.1007/s42979-024-02667-x5:4Online publication date: 27-Mar-2024
https://doi.org/10.1007/s42979-024-02667-x
Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
Wang YTao LZhang X(2023)Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical ApproachSSRN Electronic Journal10.2139/ssrn.4602954Online publication date: 2023
https://doi.org/10.2139/ssrn.4602954
Zhao SHuang ZGuo HXiao XYe R(2023)Deep Learning-Based Retrieval Algorithms for Recommendation Systems2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA)10.1109/ICPECA56706.2023.10076102(1155-1160)Online publication date: 29-Jan-2023
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Kamboj DGoel CSharma AKumar ABohra MKumar I(2023)Virtual Meet Application with Content Recommendation2023 IEEE Fifth International Conference on Advances in Electronics, Computers and Communications (ICAECC)10.1109/ICAECC59324.2023.10560264(1-6)Online publication date: 7-Sep-2023
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Gupta AShrinath P(2023)Link Prediction based on bipartite graph for recommendation system using optimized SVD++Procedia Computer Science10.1016/j.procs.2023.01.114218:C(1353-1365)Online publication date: 1-Jan-2023
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Zhou YLi QChu HLi JYang LWei BWang LYang W(2023)Inherent-Attribute-Aware Dual-Graph AutoEncoder for Rating PredictionJournal of Information and Intelligence10.1016/j.jiixd.2023.10.004Online publication date: Oct-2023
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