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Learning and adapting user criteria for recommending followees in social networks
Author(s) -
Tommasel Antonela,
Godoy Daniela
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23861
Subject(s) - novelty , computer science , weighting , rank (graph theory) , selection (genetic algorithm) , recommender system , key (lock) , diversity (politics) , factor (programming language) , information retrieval , human–computer interaction , artificial intelligence , psychology , social psychology , mathematics , medicine , computer security , combinatorics , sociology , anthropology , radiology , programming language
The accurate suggestion of interesting friends arises as a crucial issue in recommendation systems. The selection of friends or followees responds to several reasons whose importance might differ according to the characteristics and preferences of each user. Furthermore, those preferences might also change over time. Consequently, understanding how friends or followees are selected emerges as a key design factor of strategies for personalized recommendations. In this work, we argue that the criteria for recommending followees needs to be adapted and combined according to each user's behavior, preferences, and characteristics. A method is proposed for adapting such criteria to the characteristics of the previously selected followees. Moreover, the criteria can evolve over time to adapt to changes in user behavior, and broaden the diversity of the recommendation of potential followees based on novelty. Experimental evaluation showed that the proposed method improved precision results regarding static criteria weighting strategies and traditional rank aggregation techniques.