|
Robust collaborative filtering, or attack-resistant collaborative filtering, refers to algorithms or techniques that aim to make collaborative filtering more robust against efforts of manipulation, while hopefully maintaining recommendation quality. In general, these efforts of manipulation usually refer to shilling attacks, also called profile injection attacks. Collaborative filtering predicts a user's rating to items by finding similar users and looking at their ratings, and because it is possible to create nearly indefinite copies of user profiles in an online system, collaborative filtering becomes vulnerable when multiple copies of fake profiles are introduced to the system. There are several different approaches suggested to improve robustness of both model-based and memory-based collaborative filtering. However, robust collaborative filtering techniques are still an active research field, and major applications of them are yet to come. ==Introduction== One of the biggest challenges to collaborative filtering is shilling attacks. That is, malicious users or a competitor may deliberately inject certain number of fake profiles to the system (typically 1~5%) in such a way that they can affect the recommendation quality or even bias the predicted ratings on behalf of their advantages. Some of the main shilling attack strategies are random attacks, average attacks, bandwagon attacks, and segment-focused attacks. Random attacks insert profiles that give random ratings to a subset of items; average attacks give mean rating of each item.〔Bhaskar Mehta, Thomas Hofmann, and Wolfgang Nejdl, (Robust Collaborative Filtering ), RecSys ‘07 Proceedings of the 2007 ACM Conference on Recommender Systems, 49-56〕 Bandwagon and segment-focused attacks are newer and more sophisticated attack model. Bandwagon attack profiles give random rating to a subset of items and maximum rating to very popular items, in an effort to increase the chances that these fake profiles have many neighbors. Segment-focused attack is similar to bandwagon attack model, but it gives maximum rating to items that are expected to be highly rated by target user group, instead of frequently rated.〔Bamshad Mobasher, Robin Burke, Chad Williams, and Runa Bhaumik, (Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation ), Advances in Web Mining and Web Usage Analysis, 2005, 96-118〕 In general, item-based collaborative filtering is known to be more robust than user-based collaborative filtering. However, item-based collaborative filtering are still not completely immune to bandwagon and segment attacks. Robust collaborative filtering typically works as follows: # Build spam user detection model # Follow the workflow of regular Collaborative Filtering system, but only using rating data of non-spam users. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Robust collaborative filtering」の詳細全文を読む スポンサード リンク
|