| Type of Material: | Thesis |
| Title: | Metaheuristics for Collaborative Filtering in Recommender Systems |
| Researcher: | Laishram, Ayangleima. |
| Guide: | Padmanabhan Nair, Vineet C. |
| Department: | Department of Computer and Information Sciences |
| Publisher: | University of Hyderabad, Hyderabad |
| Place: | Hyderabad |
| Year: | 2021 |
| Language: | English |
| Subject: | Computer Science | Computer Science Information Systems | Engineering and Technology | Computer Science and Applications | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer and Information Sciences , University of Hyderabad, Hyderabad, Hyderabad |
| Fulltext: | Shodhganga |
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| 001 | 448595 | |
| 003 | IN-AhILN | |
| 005 | 2023-07-24 17:32:21 | |
| 008 | __ | 230724t2021||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_448595 |
| 040 | __ | |aHYDR_500046|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aLaishram, Ayangleima.|eResearcher |
| 110 | __ | |aDepartment of Computer and Information Sciences|bUniversity of Hyderabad, Hyderabad|dHyderabad|ein |
| 245 | __ | |aMetaheuristics for Collaborative Filtering in Recommender Systems |
| 260 | __ | |aHyderabad|bUniversity of Hyderabad, Hyderabad|c2021 |
| 300 | __ | |a116p|dNone |
| 502 | __ | |cDepartment of Computer and Information Sciences , University of Hyderabad, Hyderabad, Hyderabad|bPhD |
| 518 | __ | |d2021|oDate of Award |
| 518 | __ | |oDate of Registration|d2014 |
| 520 | __ | |aAbstractnewlineIt has become imperative in the current internet based era to advance technologynewlinein such a way that the preferences of individuals/users could benewlinelearned from the existing data and recommendations be made on unseennewlinedata wherein the user is satisfied with the recommended data/items to anewlinelarge extend. Recommender systems technology have been put forwardnewlineby keeping this idea in mind and several multinationals make use of thisnewlineparadigm to expand their business initiatives. In this thesis we are mainlynewlinefocused on devising methods that can improve the recommendation as wellnewlineas prediction accuracy in collaborative filtering (CF) based recommendernewlinesystems. To achieve this end we propose a variety of algorithms in whichnewlinemetaheuristic techniques are combined with matrix factorisation methodsnewlineand the combined framework is tested on two main approaches used fornewlinecollaborative filtering in recommender systems, namely, model based andn |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |2AIU|aEngineering and Technology |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Information Systems |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |eGuide|aPadmanabhan Nair, Vineet C. |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/414170|yShodhganga |
| 905 | __ | |afromsg |
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