| Title | Group attack detection in recommender systems based on triangle dense subgraph mining |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Yu, Hongtao, Yuan, Shengyu, Xu, Yishu, Ma, Ru, Gao, Dingli, Zhang, Fuzhi |
| Conference Name | 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) |
| Keywords | artificial intelligence, Computer applications, Conferences, dense subgraph mining, feature extraction, group attack detection, group shilling attacks, Human Behavior, PPR seed expansion algorithm, pubcrawl, recommender systems, resilience, Resiliency, Scalability |
| Abstract | Aiming at group shilling attacks in recommender systems, a shilling group detection approach based on triangle dense subgraph mining is proposed. First, the user relation graph is built by mining the relations among users in the rating dataset. Second, the improved triangle dense subgraph mining method and the personalizing PageRank seed expansion algorithm are used to divide candidate shilling groups. Finally, the suspicious degrees of candidate groups are calculated using several group detection indicators and the attack groups are obtained. Experiments indicate that our method has better detection performance on the Amazon and Yelp datasets than the baselines. |
| DOI | 10.1109/ICAICA52286.2021.9497958 |
| Citation Key | yu_group_2021 |
Group attack detection in recommender systems based on triangle dense subgraph mining