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Serendipitous recommendations via innovators

Published: 19 July 2010 Publication History

Abstract

To realize services that provide serendipity, this paper assesses the surprise of each user when presented recommendations. We propose a recommendation algorithm that focuses on the search time that, in the absence of any recommendation, each user would need to find a desirable and novel item by himself. Following the hypothesis that the degree of user's surprise is proportional to the estimated search time, we consider both innovators' preferences and trends for identifying items with long estimated search times. To predict which items the target user is likely to purchase in the near future, the candidate items, this algorithm weights each item that innovators have purchased and that reflect one or more current trends; it then lists them in order of decreasing weight. Experiments demonstrate that this algorithm outputs recommendations that offer high user/item coverage, a low Gini coefficient, and long estimated search times, and so offers a high degree of recommendation serendipitousness.

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Cited By

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  • (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
  • (2023)Choice models and recommender systems effects on users’ choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09366-x34:1(109-145)Online publication date: 18-May-2023
  • (2022)Biased Bytes: On the Validity of Estimating Food Consumption from Digital TracesProceedings of the ACM on Human-Computer Interaction10.1145/35556606:CSCW2(1-27)Online publication date: 11-Nov-2022
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cover image ACM Conferences
SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
July 2010
944 pages
ISBN:9781450301534
DOI:10.1145/1835449
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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Publication History

Published: 19 July 2010

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Author Tags

  1. collaborative filtering
  2. innovator
  3. personalization
  4. ranking
  5. serendipitous recommendations
  6. user flow

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SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (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
  • (2023)Choice models and recommender systems effects on users’ choicesUser Modeling and User-Adapted Interaction10.1007/s11257-023-09366-x34:1(109-145)Online publication date: 18-May-2023
  • (2022)Biased Bytes: On the Validity of Estimating Food Consumption from Digital TracesProceedings of the ACM on Human-Computer Interaction10.1145/35556606:CSCW2(1-27)Online publication date: 11-Nov-2022
  • (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
  • (2022)Boosting Item Coverage in Session-Based RecommendationServices Computing – SCC 202210.1007/978-3-031-23515-3_8(101-118)Online publication date: 10-Dec-2022
  • (2021)To Your Surprise: Identifying Serendipitous CollaboratorsIEEE Transactions on Big Data10.1109/TBDATA.2019.29215677:3(574-589)Online publication date: 1-Jul-2021
  • (2020)Venue Topic Model–enhanced Joint Graph Modelling for Citation Recommendation in Scholarly Big DataACM Transactions on Asian and Low-Resource Language Information Processing10.1145/340499520:1(1-15)Online publication date: 1-Dec-2020
  • (2020)Latent Unexpected RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/340485511:6(1-25)Online publication date: 15-Sep-2020
  • (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
  • (2020)CHESTNUT: Improve Serendipity in Movie Recommendation by an Information Theory-Based Collaborative Filtering ApproachHuman Interface and the Management of Information. Interacting with Information10.1007/978-3-030-50017-7_6(78-95)Online publication date: 10-Jul-2020
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