Premium
Followee recommendation in Twitter using fuzzy link prediction
Author(s) -
Rodríguez Fernando M.,
Torres Luis M.,
Garza Sara E.
Publication year - 2016
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12153
Subject(s) - computer science , similarity (geometry) , fuzzy logic , friendship , recommender system , information retrieval , binary number , data mining , social network (sociolinguistics) , value (mathematics) , link (geometry) , artificial intelligence , social media , machine learning , world wide web , mathematics , psychology , social psychology , computer network , arithmetic , image (mathematics)
In social networking sites, it is useful to receive recommendations about whom to contact or follow. These recommendations not only allow to establish connections with people one might already know in real life but also with people or users that have similar interests or are potentially interesting. We propose an approach that tackles contact (followee) recommendation in Twitter by means of fuzzy logic. This fuzzy approach handles recommendation as a link prediction problem and uses three types of similarity between a pair of users: tweet similarity, followee id similarity, and followee tweet similarity. These similarities are calculated by extracting user profiles. These profiles are, in turn, obtained by considering Twitter as a heterogeneous information network. To test our approach, we crawled a repository of 6000 users and two million tweets, and we measured accuracy by comparing our results with the actual followee lists of the users. These results, which are also compared against the results given by state‐of‐the‐art methods, show a high accuracy. Other advantages of the fuzzy system include a self‐explanatory capability and the ability to produce a non‐binary friendship value.