Journal article
Improved user similarity computation for finding friends in your location
Human-centric Computing and Information Sciences, Vol.8, Article number: 36
2018
Abstract
Recommender systems are most often used to predict possible ratings that a user would assign to items, in order to find and propose items of possible interest to each user. In our work, we are interested in a system that will analyze user preferences in order to find and connect people with common interests that happen to be in the same geographical area, i.e., a “friend” recommendation system. We present and propose an algorithm, Egosimilar+, which is shown to achieve superior performance against a number of well-known similarity computation methods from the literature. The algorithm adapts ideas and techniques from the recommender systems literature and the skyline queries literature and combines them with our own ideas on the importance and utilization of item popularity.
Details
- Title
- Improved user similarity computation for finding friends in your location
- Authors/Creators
- G. Tsakalakis (Author/Creator) - Technical University of CreteP. Koutsakis (Author/Creator) - Murdoch University
- Publication Details
- Human-centric Computing and Information Sciences, Vol.8, Article number: 36
- Publisher
- SpringerOpen
- Identifiers
- 991005543023307891
- Copyright
- © The Author(s) 2018
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.48 Knowledge Engineering & Representation
- 4.48.817 Recommender Systems
- Web Of Science research areas
- Computer Science, Information Systems
- ESI research areas
- Computer Science