short-paper

Personalized recommendation based on the personal innovator degree

Authors:

Noriaki Kawamae,

Hitoshi Sakano,

Takeshi YamadaAuthors Info & Claims

RecSys '09: Proceedings of the third ACM conference on Recommender systems

Pages 329 - 332

https://doi.org/10.1145/1639714.1639780

Published: 23 October 2009 Publication History

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Abstract

This paper proposes a novel Collaborative Filtering scheme; it focuses on the dynamics and precedence of user preference to recommend items that match the latest preference of the target user. In predicting which items this user will purchase in the near future, the proposed algorithm identifies purchase history logs of users who have similar preferences and a high degree of purchase precedence (i.e., purchasing the same items earlier) relative to the target user. We call this metric the Personal Innovator Degree (PID). Experiments using real online sales data show that the proposed method outperforms existing methods.

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Cited By

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Wang NChen L(2022)How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysisUser Modeling and User-Adapted Interaction10.1007/s11257-022-09350-x33:3(727-765)Online publication date: 1-Dec-2022
https://doi.org/10.1007/s11257-022-09350-x
Li PTuzhilin A(2020)Latent Unexpected RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/340485511:6(1-25)Online publication date: 15-Sep-2020
https://dl.acm.org/doi/10.1145/3404855
Pardos ZJiang WRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)Designing for serendipity in a university course recommendation systemProceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375524(350-359)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375524
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Index Terms

Personalized recommendation based on the personal innovator degree
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

RecSys '09: Proceedings of the third ACM conference on Recommender systems

October 2009

442 pages

ISBN:9781605584355

DOI:10.1145/1639714

General Chairs:
Lawrence Bergman
IBM Research, NY, USA
,
Alex Tuzhilin
New York University, NY, USA
,
Program Chairs:
Robin Burke
DePaul University, USA
,
Alexander Felfernig
Graz University of Technology, Austria
,
Lars Schmidt-Thieme
University of Hildesheim, Germany

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: 23 October 2009

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Short-paper

Conference

RecSys '09

Sponsor:

SIGCHI

RecSys '09: Third ACM Conference on Recommender Systems

October 23 - 25, 2009

New York, New York, USA

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

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Cited By

View all

Wang NChen L(2022)How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysisUser Modeling and User-Adapted Interaction10.1007/s11257-022-09350-x33:3(727-765)Online publication date: 1-Dec-2022
https://doi.org/10.1007/s11257-022-09350-x
Li PTuzhilin A(2020)Latent Unexpected RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/340485511:6(1-25)Online publication date: 15-Sep-2020
https://dl.acm.org/doi/10.1145/3404855
Pardos ZJiang WRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)Designing for serendipity in a university course recommendation systemProceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375524(350-359)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375524
Wang NChen LYang Y(2020)The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of RecommendationsProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394863(266-274)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3340631.3394863
de Gemmis MLops PPolignano M(2018)Recommender Systems, Basics ofEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_110158(2125-2137)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_110158
de Gemmis MLops PPolignano M(2017)Recommender Systems, Basics OfEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_110158-1(1-13)Online publication date: 5-Jun-2017
https://doi.org/10.1007/978-1-4614-7163-9_110158-1
Sun BHu CXu WFan H(2016)Discovering Latent Influence in Online Social Retweet Behaviors2016 IEEE First International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2016.26(296-301)Online publication date: Jun-2016
https://doi.org/10.1109/DSC.2016.26
Ren YLi GZhou W(2015)A survey of recommendation techniques based on offline data processingConcurrency and Computation: Practice & Experience10.1002/cpe.337027:15(3915-3942)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1002/cpe.3370
Adamopoulos PTuzhilin A(2014)On Unexpectedness in Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/25599525:4(1-32)Online publication date: 18-Dec-2014
https://dl.acm.org/doi/10.1145/2559952
Foulonneau MGrouès VNaudet YChevalier M(2014)Recommender Systems and Diversity: Taking Advantage of the Long Tail and the Diversity of Recommendation ListsRecommender Systems10.1002/9781119054252.ch4(71-92)Online publication date: 5-Dec-2014
https://doi.org/10.1002/9781119054252.ch4
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