Please use this identifier to cite or link to this item: http://10.9.150.37:8080/dspace//handle/atmiyauni/936
Title: Recommender system: Techniques, comparison & solutions
Authors: Gohel, Divyesh
Vanjara, Pratik
Keywords: Recommender system
Data collection
Content based
collaborative
Hybrid filtration systems
Issue Date: Apr-2022
Publisher: 2022 IEEE 7th International conference for Convergence in Technology (I2CT)
Citation: Gohel, D., & Vanjara, P. (2022, April). Recommender System: Techniques, Comparison & Solutions. In 2022 IEEE 7th International conference for Convergence in Technology (I2CT) (pp. 1-7). IEEE.
Abstract: There are several benefits of e-commerce websites that include cost effectiveness, convenience, flexibility, fast delivery, increase in income, etc. With these benefits, there is crucial role of e-commerce websites in business and users. However, e-commerce websites produce an overload of data, hence, Recommender Systems (RSs) provides a solution for the data overload problem. The present study, reviews different types of RSs and its pros and cons. Then, it does comparative study of different types of RSs. After the review, it’s concluded that collaborating filtering technique used more than all other ones in e-commerce websites. There are problems with almost all techniques including the collaborative filtering technique too. However there is a novel model proposed that fixes the collaborative filtering technique of ‘cold start’ at its best.
Description: At last, I would like to express my heartfelt thankfulness to the Computer Science & Information Technology department for their valuable advice and support.
URI: http://10.9.150.37:8080/dspace//handle/atmiyauni/936
ISSN: 6654-2168
Appears in Collections:01. Journal Articles

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