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Title: | Recommendation system models for e-commerce systems: a scenario in gift purchase for family and friends | Authors: | Nguyen Gia Kim Khiem | Keywords: | Recommendation system;Implicit feedback;E-commerce | Issue Date: | 2022 | Abstract: | The emergence of recommendation systems has become a critical component in various fields, transforming the way that people approach and engage with services and products. As the number of choices keeps increasing with the development of society, RS provides helpful guidance in navigating the array of choices to locate items that align with the preferences and requirements of specific individuals. With the rapid expansion of the e-commerce field, the work of optimizing and simplifying the choices that a user may encounter can make the experience more approachable and more personalized. Leveraging recommendation engines can help with decision fatigue and information overload and improve the user experience. However, as the information on user preference is not readily available through comments or ratings that users may leave, using a traditional recommendation system, which often relies on explicit feedback, may not yield accurate recommendations. To tackle this problem, a recommendation engine that only requires user interactions in order to make effective recommendations is essential in retaining and increasing the number of loyal customers. This notion can also be extended to the case of recommending gifts to enhance user fidelity. The aim of the thesis is to present methods to utilize only implicit data to make recommendations based on the interaction of customers with items. The algorithms utilized will be compared and discussed on the benefits and disadvantages that each of the algorithms will have |
URI(1): | https://epub.vgu.edu.vn/handle/dlibvgu/1767 | Rights: | Attribution-ShareAlike 3.0 United States |
Appears in Collections: | Computer Science (CS) |
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Recommendation system models for e-commerce systems a scenario in gift purchase for family and friends.pdf | 1.36 MB | Adobe PDF |
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