| Type of Material: | Thesis |
| Title: | Use of machine learning algorithm to optimize the performance of recommendation system for e commerce by alleviating cold start problem and shilling attack |
| Researcher: | Singh, Anurag |
| Guide: | Shaw, Subhadra |
| Department: | Department of Computer Science |
| Publisher: | A N Sinha Institute of Social Studies, Patna, Bihar |
| Place: | Patna |
| Year: | 2024 |
| Language: | English |
| Subject: | Computer Science | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer Science, A N Sinha Institute of Social Studies, Patna, Bihar, Patna; 2024 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-08-28 15:20:30 | |
| 008 | __ | 260828t2024||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_462459 |
| 040 | __ | |dIN-AhILN|aANSI_800001 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aSingh, Anurag |
| 110 | __ | |aDepartment of Computer Science|0C-57226|dPatna|bA N Sinha Institute of Social Studies, Patna, Bihar|ein |
| 245 | __ | |aUse of machine learning algorithm to optimize the performance of recommendation system for e commerce by alleviating cold start problem and shilling attack |
| 260 | __ | |aPatna|c2024|bA N Sinha Institute of Social Studies, Patna, Bihar |
| 300 | __ | |a100 p.|dDVD|c30*20 |
| 502 | __ | |bPhD|d2024|cDepartment of Computer Science, A N Sinha Institute of Social Studies, Patna, Bihar, Patna |
| 518 | __ | |oDate of Award|d2024 |
| 518 | __ | |oDate of Registration|d2019 |
| 518 | __ | |oDate of Completion|d2024 |
| 520 | __ | |aA recommender system is an integral part of any e-commerce site. Recommender systems are currently highly relevant for helping users deal with the information overload they suffer from the large volume of data on the web, and automatically suggest the most appropriate items that meet user s needs. However, in cases in which a user is new to the recommender system, the system cannot recommend items that are relevant to her/him because of a lack of previous information about the user and/or the user-item rating history that helps to determine the user s preferences. This problem is known as cold-start, which remains open because it does not have a final solution. Shilling attacks are among the essential challenges in recommender systems, which use the creation of fake profiles in the system and biased rating of items, causing the accuracy to decrease and the correct performance of the recommender system in providing recommendations to users. The target of attackers is to change the rank of content or items co |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 700 | __ | |aShaw, Subhadra|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/557675|yShodhganga |
| 905 | __ | |afromsg |
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