Combining User-Based and Item-Based Collaborative Filtering Using Machine Learning

Autor:	Vijay Ukani, Sudeep Tanwar, Krunal Varma, Priyank Thakkar, Sapan H. Mankad
Rok vydání:	2018
Předmět:	Support vector machine Computer science business.industry Mean squared prediction error Linear regression Collaborative filtering Artificial intelligence business Machine learning computer.software_genre computer
Zdroj:	Information and Communication Technology for Intelligent Systems ISBN: 9789811317460
Popis:	Collaborative filtering (CF) is typically used for recommending those items to a user which other like-minded users preferred in the past. User-based collaborative filtering (UbCF) and item-based collaborative filtering (IbCF) are two types of CF with a common objective of estimating target user’s rating for the target item. This paper explores different ways of combining predictions from UbCF and IbCF with an aim of minimizing overall prediction error. In this paper, we propose an approach for combining predictions from UbCF and IbCF through multiple linear regression (MLR) and support vector regression (SVR). Results of the proposed approach are compared with the results of other fusion approaches. The comparison demonstrates the superiority of the proposed approach. All the tests are performed on a large publically available dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::95731fea4cc9881b3cc40c0ba9dd8c6e https://doi.org/10.1007/978-981-13-1747-7_17 Zobrazit plný text záznamu