Enhancing Social Recommenders with Implicit Preferences and Fuzzy Confidence Functions

Autor:	Nicolás Hernández, Haydemar Núñez, Camilo A. Franco
Rok vydání:	2021
Předmět:	Estimation Computer science business.industry Recommender system Machine learning computer.software_genre Fuzzy logic Measure (mathematics) Social relation k-nearest neighbors algorithm Test (assessment) Collaborative filtering Artificial intelligence business computer
Zdroj:	Modeling Decisions for Artificial Intelligence ISBN: 9783030855284 MDAI
DOI:	10.1007/978-3-030-85529-1_10
Popis:	In this paper we explore the use of fuzzy confidence functions for enhancing the performance of social recommenders with implicit preferences, focusing on K-Nearest Neighbors (KNN) collaborative filtering algorithms. Firstly, we measure the effects of including social relations for enhancing the performance of the algorithms with either explicit or implicit preferences, expecting to verify better results when social attributes are considered in the relevant neighborhood estimation. Secondly, it is proposed to enhance the social recomenders with implicit preferences by fuzzy confidence functions. An application is developed to measure the effects of including social relations and to illustrate our proposal on the fuzzy modeling of implicit preferences, recommending courses based on the students socio-demographic and academic information. As a result, the best recommendations are accomplished with socially-enhanced algorithms that make use of implicit preferences and fuzzy confidence functions, obtaining a FCP in test of 0.68.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d93327e5ab476c4f1c245e39591a9695 https://doi.org/10.1007/978-3-030-85529-1_10 Zobrazit plný text záznamu