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Social Commerce Recommendation Systems: Leveraging User Behaviour and Preferences
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Author(s): Aanchal Taliwal (University of Petroleum and Energy Studies, India), Mimansa Pathania (University of Petroleum and Energy Studies, India)and Mitali Chugh (University of Petroleum and Energy Studies, India)
Copyright: 2024
Pages: 21
Source title:
Advanced Applications of Generative AI and Natural Language Processing Models
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Bharat Bhushan (School of Engineering and Technology, Sharda University, India), Muthmainnah S. (Universitas Al Asyariah Mandar, Indonesia)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-0502-7.ch013
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Abstract
Recommender systems are software tools that make recommendations based on user needs and are increasingly popular in both commercial and research settings, with various approaches being suggested for providing recommendations. To choose the appropriate algorithm, system designers must focus on specific properties of the application, such as accuracy, robustness, and scalability. Comparative studies are used to compare algorithms, and experimental settings are described. The chapter discusses the importance of understanding user acceptance of recommendations provided by recommender systems and the influence of source characteristics in human-human, human-computer, and human-recommender system interactions. This chapter contributes to the study of social commerce by assessing the effects of the social web on different stages of purchase decision making and presents a model for analyzing social commerce.
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