Advancing Recommender Systems by mitigating Shilling attacks
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913328760094720 |
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| author | Chichani, Aditya Golwala, Juzer Gundecha, Tejas Gawande, Kiran |
| author_facet | Chichani, Aditya Golwala, Juzer Gundecha, Tejas Gawande, Kiran |
| contents | Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good performance. But, this method makes the system vulnerable to attacks which try to bias the recommendations. These attacks, known as 'shilling attacks' are performed to push an item or nuke an item in the system. This paper proposes an algorithm to detect such shilling profiles in the system accurately and also study the effects of such profiles on the recommendations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16177 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Recommender Systems by mitigating Shilling attacks Chichani, Aditya Golwala, Juzer Gundecha, Tejas Gawande, Kiran Information Retrieval Artificial Intelligence Cryptography and Security Machine Learning Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good performance. But, this method makes the system vulnerable to attacks which try to bias the recommendations. These attacks, known as 'shilling attacks' are performed to push an item or nuke an item in the system. This paper proposes an algorithm to detect such shilling profiles in the system accurately and also study the effects of such profiles on the recommendations. |
| title | Advancing Recommender Systems by mitigating Shilling attacks |
| topic | Information Retrieval Artificial Intelligence Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2404.16177 |