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PITTCULT: trust-based cultural event recommender

Published: 23 October 2008 Publication History

Abstract

Typical collaborative filtering recommenders (CF) do not provide any chance for users to choose or evaluate the bases for recommendation. Once the system evaluates a group of users as being similar to a target user, her information is tailored by unknown people's taste. As a cultural event recommender, PITTCULT provides a way for users to rate the trustworthiness of other users; then, according to those ratings, a recommendation is generated. This paper explains why trust-based recommendation is necessary, and how studies using PITTCULT cope with the problems of the existing CF.

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cover image ACM Conferences
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
October 2008
348 pages
ISBN:9781605580937
DOI:10.1145/1454008
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: 23 October 2008

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Author Tags

  1. cultural event recommender
  2. personalized information
  3. trusted-based recommendation

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RecSys08: ACM Conference on Recommender Systems
October 23 - 25, 2008
Lausanne, Switzerland

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

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  • (2020)Group Behavior RecognitionHuman Behavior Analysis: Sensing and Understanding10.1007/978-981-15-2109-6_6(139-218)Online publication date: 1-Mar-2020
  • (2017)Finding suitable places for live campaigns using location-based servicesProceedings of the Fourth International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/3080546.3080630(1-6)Online publication date: 14-May-2017
  • (2017)Interacting with Recommenders—Overview and Research DirectionsACM Transactions on Interactive Intelligent Systems10.1145/30018377:3(1-46)Online publication date: 19-Sep-2017
  • (2017)Mobile and Context-Aware Event Recommender SystemsWeb Information Systems and Technologies10.1007/978-3-319-66468-2_8(142-163)Online publication date: 8-Sep-2017
  • (2016)CPERS: Contextual and Personalized Event Recommender System2016 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI.2016.0086(421-426)Online publication date: Dec-2016
  • (2014)Predicting activity attendance in event-based social networksProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/2632048.2632063(425-434)Online publication date: 13-Sep-2014
  • (2014)Should I Stay or Should I Go? Improving Event Recommendation in the Social WebInteracting with Computers10.1093/iwc/iwu02928:1(55-72)Online publication date: 21-Aug-2014
  • (2013)Hybrid event recommendation using linked data and user diversityProceedings of the 7th ACM conference on Recommender systems10.1145/2507157.2507171(185-192)Online publication date: 12-Oct-2013
  • (2013)RecSys for distributed eventsProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484119(953-956)Online publication date: 28-Jul-2013
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