Optimal Rating Prediction in Recommender Systems

Autor:	Bilal Ahmed, Waqar Hussain, M. Abdul Qadoos, Muhammad Amjad, Zheng Tingyi, Li Wang, Akbar Hussain, Muhammad Raheel, Syed Badar-ud-Duja
Rok vydání:	2020
Předmět:	business.industry Computer science Feature selection Recommender system Machine learning computer.software_genre Preference Information overload Field (computer science) MovieLens Collaborative filtering The Internet Artificial intelligence business computer
Zdroj:	Communications in Computer and Information Science ISBN: 9789811528095 ICDS
DOI:	10.1007/978-981-15-2810-1_32
Popis:	Recommendation systems are best choice to cope with the problem of information overload. These systems are commonly used in recent years help to match users with different items. The increasing amount of available data on internet in recent year’s pretenses some great challenges in the field of recommender systems. Main challenge is to predict the user preference and provide favorable recommendations. In this article, we present a new mechanism to improve the prediction accuracy in recommendations. Our method includes a discretization step and chi-square algorithm for attribute selection. Results on MovieLens dataset show that our technique performs well and minimize the error ratio.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::fa1e02b76bc1e43555f5861abe34d0de https://doi.org/10.1007/978-981-15-2810-1_32 Zobrazit plný text záznamu