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  1. Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation (CaRR '11)
  2. Utilizing implicit feedback and context to recommend mobile applications from first use
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Innovative Retail Laboratory: assistive technologies in retail environments
Towards a context-sensitive online newspaper
Implicit acquisition of context for personalization of information retrieval systems
Real-time, location-aware collaborative filtering of web content
Utilizing implicit feedback and context to recommend mobile applications from first use
Context-aware POI recommendations in an automotive scenario using multi-criteria decision making methods
RecLab: a system for eCommerce recommender research with real data, context and feedback
OSUSUME: cross-lingual recommender system for research papers
Designing activity-aware recommender systems for operating rooms
Guidance and support for healthy food preparation in an augmented kitchen

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Utilizing implicit feedback and context to recommend mobile applications from first use

Content Provider ACM Digital Library
Author Moritz, Simon Davidsson, Christoffer
Abstract Most mobile platforms of today enable the users to install third-party applications through application portals or stores. As the number of applications available increases, the users of mobile devices find it challenging to find new and relevant applications. The fact that these applications usually are browsed and downloaded from a mobile device, which has a smaller screen compared to desktop computers, makes this information overload even more intense. Recommender systems aid users in finding relevant applications. A challenge with such systems is that they traditionally need a user profile in order to produce recommendations, known as the new user problem. In this paper we present a context-aware recommender system for mobile applications which produces recommendations from the first use. This paper introduces context-based recommender concepts and presents a prototype implementation of said concepts.
Starting Page 19
Ending Page 22
Page Count 4
File Format PDF
ISBN 9781450306256
DOI 10.1145/1961634.1961639
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-02-13
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Context-aware Mobile applications Recommender system Android
Content Type Text
Resource Type Article
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