Recommendation Systems for E-Commerce - Hakimian, Hamed
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Nowadays, Recommendation Systems are becoming increasingly vital to the Web users to identify products, services or contents that they might like. Recommendation Systems using Collaborative Filtering are very vulnerable to the Cold-Start problem because they operate solely on the basis of users' preferences. Hence, researches have recently proposed efficient hybrid solutions, so called Hybrid Recommendation Systems , that combine both Content-based Filtering and Collaborative Filtering to boost the performance. This book attempts to resolve the Cold-Start problem along with implementing a web…mehr

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Produktbeschreibung
Nowadays, Recommendation Systems are becoming increasingly vital to the Web users to identify products, services or contents that they might like. Recommendation Systems using Collaborative Filtering are very vulnerable to the Cold-Start problem because they operate solely on the basis of users' preferences. Hence, researches have recently proposed efficient hybrid solutions, so called Hybrid Recommendation Systems , that combine both Content-based Filtering and Collaborative Filtering to boost the performance. This book attempts to resolve the Cold-Start problem along with implementing a web application working based on information of users' profiles. Finally, to do business analysis, the results of questionnaires and interviews have been brought.
Autorenporträt
Hamed Hakimian ist derzeit Forscher in den Bereichen Business Intelligence, International Business, E-Commerce und Informationssystem-Empfehlungssystem. Er erhielt 2016 einen Master of Business International Business von der University of Putra Malaysia.