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New perspectives on gray sheep behavior in E-commerce recommendations
دیدگاه های جدید در مورد رفتار گوسفند خاکستری در توصیه های تجارت الکترونیکی-2019 With the exponential rise in the size of data being generated, personalization based on recommender systems has
become an important aspect of digital marketing strategy of E-Commerce companies. Recommender systems also
help these companies in cross-selling, up-selling and to increase the customer loyalty. However, presence of
certain users, known as gray sheep users, with eccentric taste, minimizes the overall efficiency of the recommender
systems. Hence, their identification and removal from the computation system is critical for more
efficient recommendations. This work presents psychographic models-based approaches for gray sheep user
identification with improved performance. It also studies gray sheep behavior across different domains and
contexts, apart from introducing the idea of gray sheep items Keywords: Personalization | Gray Sheep Users | Psychographic models | Cross Domain Recommender Systems | Context Aware Recommender Systems | Collaborative Filtering |
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