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User Profiling Trends, Techniques and Applications
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The main objective of this paper is to explore the field of personalization in context of user profiling, to help researchers make aware of the user profiling.Abstract:
The Personalization of information has taken recommender systems at a very high level. With personalization these systems can generate user specific recommendations accurately and efficiently. User profiling helps personalization, where information retrieval is done to personalize a scenario which maintains a separate user profile for individual user. The main objective of this paper is to explore this field of personalization in context of user profiling, to help researchers make aware of the user profiling. Various trends, techniques and Applications have been discussed in paper which will fulfill this motto.read more
Citations
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Journal ArticleDOI
A Survey of User Profiling: State-of-the-Art, Challenges, and Solutions
TL;DR: The findings showed that an effective modeling process enhances the construction of accurate user profile for service personalization.
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TL;DR: This research effort builds an updated catalog of the existing water demand datasets to facilitate future research efforts and encourage the publication of open-access datasets in water demand modelling and management research.
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Application of Internet of Things and artificial intelligence for smart fitness: A survey
TL;DR: A comprehensive review on different types of fitness trackers and fitness applications in provided and followed by a review of AI algorithms used in smart fitness scenarios is presented and a shortlist of existing gaps and potential future work have been identified and proposed.
Journal ArticleDOI
SOBER-MCS: Sociability-Oriented and Battery Efficient Recruitment for Mobile Crowd-Sensing.
Fazel Anjomshoa,Burak Kantarci +1 more
TL;DR: A new social activity-aware recruitment policy, namely Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS), which is able to introduce battery savings up to 18.5% while improving user and platform utilities by 12% and 20%, respectively.
Proceedings ArticleDOI
Recommending News Based on Hybrid User Profile, Popularity, Trends, and Location
Suraj Natarajan,Melody Moh +1 more
TL;DR: The proposed system is a successful example of incorporating temporal dynamics to recommendation systems; the combination of using hybrid user profile, popularity, trends and location would have significant impact on other recommendation systems in the future.
References
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Journal ArticleDOI
Implicit feedback for inferring user preference: a bibliography
Diane Kelly,Jaime Teevan +1 more
TL;DR: Traditional relevance feedback methods require that users explicitly give feedback by specifying keywords, selecting and marking documents, or answering questions about their interests, which can be difficult to collect the necessary data and the effectiveness of explicit techniques can be limited.
Journal ArticleDOI
Ontological user profiling in recommender systems
TL;DR: Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy are shown.
Proceedings ArticleDOI
Towards social user profiling: unified and discriminative influence model for inferring home locations
TL;DR: A unified discriminative influence model, named as UDI, is proposed to solve the problem of profiling users' home locations in the context of social network (Twitter), and develops local and global location prediction methods.
Proceedings Article
Combining data mining and machine learning for effective user profiling
Tom Fawcett,Foster Provost +1 more
TL;DR: This paper combines data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior, and uses a rule-learning program to uncover indicators of fraudulent behavior from a large database of cellular calls.
Journal ArticleDOI
A Combination Approach to Web User Profiling
TL;DR: This article formalizes the profiling problem as several subtasks: profile extraction, profile integration, and user interest discovery, and proposes a combination approach to deal with the profiling tasks.