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Recommender system: Techniques, comparison & solutions

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dc.contributor.author Gohel, Divyesh
dc.contributor.author Vanjara, Pratik
dc.date.accessioned 2023-05-15T05:52:06Z
dc.date.available 2023-05-15T05:52:06Z
dc.date.issued 2022-04
dc.identifier.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. en_US
dc.identifier.issn 6654-2168
dc.identifier.uri http://10.9.150.37:8080/dspace//handle/atmiyauni/936
dc.description At last, I would like to express my heartfelt thankfulness to the Computer Science & Information Technology department for their valuable advice and support. en_US
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher 2022 IEEE 7th International conference for Convergence in Technology (I2CT) en_US
dc.subject Recommender system en_US
dc.subject Data collection en_US
dc.subject Content based en_US
dc.subject collaborative en_US
dc.subject Hybrid filtration systems en_US
dc.title Recommender system: Techniques, comparison & solutions en_US
dc.type Article en_US


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