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Social recommendation: A user profile clustering‐based approach
Author(s) -
Ouaftouh Sara,
Zellou Ahmed,
Idri Ali
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5330
Subject(s) - collaborative filtering , computer science , cluster analysis , recommender system , user profile , information retrieval , user modeling , data mining , similarity (geometry) , set (abstract data type) , information filtering system , context (archaeology) , world wide web , machine learning , user interface , artificial intelligence , programming language , operating system , paleontology , biology , image (mathematics)
Summary The recommendation in information systems is a specific form of information filtering that aims to present the relevant information interesting the user. This technique is used in different contexts such as social networking, e‐commerce and information retrieval. Generally, existing recommender system techniques implement collaborative filtering by deducing a part of user interests from the preferences of other users with similar profiles. Many techniques can be used to implement Collaborative Filtering such as Bayesian Networks, latent semantic, and clustering. We present in this work a novel clustering approach using a modified partitional algorithm. We propose a user model that integrates the relevant user information and a clustering algorithm that generates groups of similar user profiles by implementing a profile similarity function. The proposed approach is then evaluated based on a set of user profiles data corresponding to the context of an e‐commerce website.