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A multi-stage collaborative filtering approach for mobile recommendation

Published: 15 February 2009 Publication History

Abstract

Location-based and personalized services are the key factors for promoting user satisfaction. However, most service providers did not consider the needs of mobile user in terms of their location and event-participation. Consequently, the service provider may lose the chance for better service and profit. In this paper, we present a Multi-stage Collaborative Filtering (MSCF) process to provide event recommendation based on mobile user's location. To achieve this purpose, the Collaborative Filtering (CF) technique is employed and the Adaptive Resonance Theory (ART) network is applied to cluster mobile users according to their personal profile. Sequential pattern mining is, then, used to discover the correlations between events for recommendation. The MSCF is designed not only to recommend for the old registered mobile user (ORMU), but also to handle the cold-start problem for new registered mobile user (NRMU). This research is designed to achieve the followings.
(1) To present a personalized event recommendation system for mobile users.
(2) To discover mobile users' moving patterns.
(3) To provide recommendations based on mobile users' preferences.
(4) To overcome the cold-start problem for new registered mobile user.
The experimental results of this research show that the MSCF is able to accomplish the above purposes and shows better outcome for cold-start problem when comparing with user-based CF and item-based CF.

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  1. A multi-stage collaborative filtering approach for mobile recommendation

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      cover image ACM Conferences
      ICUIMC '09: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
      February 2009
      704 pages
      ISBN:9781605584058
      DOI:10.1145/1516241
      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: 15 February 2009

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      Author Tags

      1. adaptive resonance theory (ART)
      2. collaborative filtering(CF)
      3. event recommendation
      4. location-based
      5. recommendation systems (RS)
      6. sequential pattern mining

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      • (2017)Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic ModelIEEE Transactions on Multimedia10.1109/TMM.2017.265207419:6(1314-1326)Online publication date: 1-Jun-2017
      • (2017)Context-aware recommender systems in mobile environmentInformation Systems10.1016/j.is.2017.09.00172:C(27-61)Online publication date: 1-Dec-2017
      • (2016)Context-Aware Location Recommendation Using Geotagged Photos in Social MediaISPRS International Journal of Geo-Information10.3390/ijgi51101955:11(195)Online publication date: 28-Oct-2016
      • (2015)A Recommender System for Mobile Commerce Based on Relational LearningMulti-disciplinary Trends in Artificial Intelligence10.1007/978-3-319-26181-2_39(415-428)Online publication date: 29-Nov-2015
      • (2012)A Context-aware Collaborative Filtering Approach for Service RecommendationProceedings of the 2012 International Conference on Cloud and Service Computing10.1109/CSC.2012.30(148-155)Online publication date: 22-Nov-2012
      • (2012)Using Context-Aware Collaborative Filtering for POI Recommendations in Mobile GuidesAdvances in Location-Based Services10.1007/978-3-642-24198-7_9(131-147)Online publication date: 2012
      • (2011)Mobile commerce product recommendations based on hybrid multiple channelsElectronic Commerce Research and Applications10.1016/j.elerap.2010.08.00410:1(94-104)Online publication date: 1-Jan-2011

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