Please use this identifier to cite or link to this item: http://10.9.150.37:8080/dspace//handle/atmiyauni/958
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dc.contributor.authorGohel, Divyesh-
dc.contributor.authorVanjara, Pratik-
dc.date.accessioned2023-05-16T04:09:42Z-
dc.date.available2023-05-16T04:09:42Z-
dc.date.issued2022-04-
dc.identifier.citationGohel, 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.issn6654-2168-
dc.identifier.urihttp://10.9.150.37:8080/dspace//handle/atmiyauni/958-
dc.description.abstractThere 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.isoenen_US
dc.publisher2022 IEEE 7th International conference for Convergence in Technology (I2CT)en_US
dc.subjectRecommender systemen_US
dc.subjectContent baseden_US
dc.subjectData collectionen_US
dc.subjectcollaborativeen_US
dc.subjectHybrid filtration systemsen_US
dc.titleRecommender system: Techniques, comparison & solutionsen_US
dc.typeArticleen_US
Appears in Collections:01. VSC CSIT FP Journal Articles

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