Authors:
Mohammed Al-Zeyadi
;
Frans Coenen
and
Alexei Lisitsa
Affiliation:
University of Liverpool, United Kingdom
Keyword(s):
Movement Pattern Mining, Social Networks, Recommender Systems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Collaborative Filtering
;
Concept Mining
;
Data Analytics
;
Data Engineering
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
Dating Social Networks (DSN) have become a popular platform for people to look for potential romantic partners. However, the main challenge is the size of the dating network in terms of the number of registered users, which makes it impossible for users to conduct extensive searches. DSN systems thus make recommendations, typically based on user profiles, preferences and behaviours. The provision of effective User-to-User recommendation systems have thus become an essential part of successful dating networks. To date the most commonly used recommendation technique is founded on the concept of collaborative filtering. In this paper an alternative approach, founded on the concept of Movement Patterns, is presented. A movement pattern is a three-part pattern that captures the “traffic” (messaging) between vertices (users) in a DSN. The idea is that these capture the behaviour of users within a DSN while at the same time capturing the associated profile and preference data. The idea has
been built into a User-to-User recommender system, the RecoMP system. The system has been evaluated, by comparing its operation with a collaborative filtering systems (the RecoCF system), using a data set from the Chinese Jiayuan.com DSN comprising 548,395 vertices. The reported evaluation demonstrates that very successful results can be produced, a best average F-score value of 0.961.
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