Robust collaborative filtering

Mehta, Bhaskar; Hofmann, Thomas; Nejdl, Wolfgang

doi:10.1145/1297231.1297240

Cited by 82 publications

(63 citation statements)

References 22 publications

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“…Lops makes the criticism that collaborative filtering systems are black boxes that cannot explain why an item is recommended except that other users liked it [262]. Manipulation is also a problem: since collaborative filtering is based on user opinions, blackguards might try to manipulate ratings to promote their products so they are recommended more often [333][334][335].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

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Section: Collaborative Filteringmentioning

confidence: 99%

Research-paper recommender systems: a literature survey

et al. 2015

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“…In order to improve the attack-resistant ability of collaborative recommender systems, researchers have proposed many shilling attack detection algorithms [4][5][6] and robust recommendation algorithms [7][8][9][10]. Mehta et al proposed shilling attack detection algorithm using principal component analysis (PCA) of the users' profiles [4].…”

Section: Introductionmentioning

confidence: 99%

“…Mehta et al proposed a robust recommendation algorithm (RMF) based on M-estimators [7]. The reference [8] proposed a least trimmed squares estimator based matrix factorization (LTSMF) algorithm, which shows better robustness compared with RMF.…”

Section: Introductionmentioning

confidence: 99%

Robust Recommendation Method Based on Shilling Attack Detection and Matrix Factorization Model

Hu¹,

Li²,

Zhang³

2018

dtcse

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Abstract. The existing robust collaborative recommendation algorithms have low robustness against PIA and AoP attacks. Aiming at the problem, we propose a robust recommendation method based on shilling attack detection and matrix factorization model. Firstly, the type of shilling attack is identified based on statistical characteristics of attack profiles. Secondly, we devise corresponding unsupervised detection algorithms for standard attack, AoP and PIA, and the suspicious users and items are flagged. Finally, we devise a robust recommendation algorithm by combining the proposed shilling attack detection algorithm with matrix factorization model, and conduct experiments on the MovieLens dataset to demonstrate its effectiveness. Experimental results show that the proposed method exhibits good recommendation precision and excellent robustness for shilling attacks of multiple types.

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“…CF technology has turned out not to be well protected against malicious users who try to harm the system or to make a profit by gamming corresponding recommender systems. For example, a malicious user who copies the entire profiles of target users can mislead recommender systems into thinking the malicious user is a perfect cohort and then recommending his/her products to target users [9][10][11]. A group of ad-hoc users are also able to reinforce their own ratings and shift the recommendation predictions in the direction(s) that they desire so [12].…”

Section: Introductionmentioning

confidence: 99%

Personalizing Information Using Users’ Online Social Networks: A Case Study of CiteULike

Lee

2015

J Inf Process Syst

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This paper aims to assess the feasibility of a new and less-focused type of online sociability (the watching network) as a useful information source for personalized recommendations. In this paper, we recommend scientific articles of interests by using the shared interests between target users and their watching connections. Our recommendations are based on one typical social bookmarking system, CiteULike. The watching network-based recommendations, which use a much smaller size of user data, produces suggestions that are as good as the conventional Collaborative Filtering technique. The results demonstrate that the watching network is a useful information source and a feasible foundation for information personalization. Furthermore, the watching network is substitutable for anonymous peers of the Collaborative Filtering recommendations. This study shows the expandability of social network-based recommendations to the new type of online social networks.

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Robust collaborative filtering

Cited by 82 publications

References 22 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

Robust Recommendation Method Based on Shilling Attack Detection and Matrix Factorization Model

Personalizing Information Using Users’ Online Social Networks: A Case Study of CiteULike

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