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108
A Survey of Collaborative Filtering Techniques
, 2009
"... As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenge ..."
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Cited by 216 (0 self)
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As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Collaborative Filtering Recommender Systems
, 2007
"... One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on ..."
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Cited by 113 (2 self)
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One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.
Generic User Modeling Systems
, 2001
"... The paper reviews the development of generic user modeling systems over the past twenty years. It describes their purposes, their services within user-adaptive systems, and the different design requirements for research prototypes and commercially deployed servers. It discusses the architectures tha ..."
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Cited by 57 (0 self)
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The paper reviews the development of generic user modeling systems over the past twenty years. It describes their purposes, their services within user-adaptive systems, and the different design requirements for research prototypes and commercially deployed servers. It discusses the architectures that have been explored so far, namely shell systems that form part ofthe application, central server systems that communicate with several applications, and possible future user modeling agents that physically follow the user. Several implemented research prototypes and commercial systems are briefly described.
Preventing Shilling Attacks in Online Recommender Systems
- IN WIDM ’05: PROCEEDINGS OF THE 7TH ANNUAL ACM INTERNATIONAL WORKSHOP ON WEB INFORMATION AND DATA MANAGEMENT
, 2005
"... Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with careful ..."
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Cited by 41 (3 self)
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Collaborative filtering techniques have been successfully employed in recommender systems in order to help users deal with information overload by making high quality personalized recommendations. However, such systems have been shown to be vulnerable to attacks in which malicious users with carefully chosen profiles are inserted into the system in order to push the predictions of some targeted items. In this paper we propose several metrics for analyzing rating patterns of malicious users and evaluate their potential for detecting such shilling attacks. Building upon these results, we propose and evaluate an algorithm for protecting recommender systems against shilling attacks. The algorithm can be employed for monitoring user ratings and removing shilling attacker profiles from the process of computing recommendations, thus maintaining the high quality of the recommendations.
Do you trust your recommendations? An exploration of security and privacy issues in recommender systems
- in Proc. of ETRICS’06, ser. LNCS
, 2006
"... Abstract. Recommender systems are widely used to help deal with the problem of information overload. However, recommenders raise serious privacy and security issues. The personal information collected by recommenders raises the risk of unwanted exposure of that information. Also, malicious users can ..."
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Cited by 29 (0 self)
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Abstract. Recommender systems are widely used to help deal with the problem of information overload. However, recommenders raise serious privacy and security issues. The personal information collected by recommenders raises the risk of unwanted exposure of that information. Also, malicious users can bias or sabotage the recommendations that are provided to other users. This paper raises important research questions in three topics relating to exposure and bias in recommender systems: the value and risks of the preference information shared with a recommender, the effectiveness of shilling attacks designed to bias a recommender, and the issues involved in distributed or peer-to-peer recommenders. The goal of the paper is to bring these questions to the attention of the information and communication security community, to invite their expertise in addressing them. 1
Privacy-Enhanced Web Personalization
- In P. Brusilovsky, A. Kobsa & W. Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization
, 2007
"... Abstract. Consumer studies demonstrate that online users value personalized content. At the same time, providing personalization on websites seems quite profitable for web vendors. This win-win situation is however marred by privacy concerns since personalizing people's interaction entails gath ..."
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Cited by 27 (7 self)
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Abstract. Consumer studies demonstrate that online users value personalized content. At the same time, providing personalization on websites seems quite profitable for web vendors. This win-win situation is however marred by privacy concerns since personalizing people's interaction entails gathering considerable amounts of data about them. As numerous recent surveys have consistently demonstrated, computer users are very concerned about their privacy on the Internet. Moreover, the collection of personal data is also subject to legal regulations in many countries and states. Both user concerns and privacy regulations impact frequently used personalization methods. This article analyzes the tension between personalization and privacy, and presents approaches to reconcile the both. It has been tacitly acknowledged for many years that personalized interaction and user modeling have significant privacy implications, due to the fact that large amounts of personal information about users needs to be collected to perform personalization. For
Distributed Collaborative Filtering for Peer-to-Peer File Sharing Systems
- In Proceedings of the 21st Annual ACM Symposium on Applied Computing
, 2006
"... Peer-to-peer networks are becoming more and more popular to share information such as, for example, multimedia files. Since this information is stored locally at the different peers, it is necessary to facilitate the search in an intelligent way. Collaborative filtering is such a search technique th ..."
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Cited by 26 (2 self)
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Peer-to-peer networks are becoming more and more popular to share information such as, for example, multimedia files. Since this information is stored locally at the different peers, it is necessary to facilitate the search in an intelligent way. Collaborative filtering is such a search technique that enables to incorporate the preferences of a user that can be learned from the download activities of the users. To be effective collaborative filtering requires a large database that captures these activities. Within a peerto-peer network this is, however, not readily available. Here, we propose a collaborative filtering approach that is self-organizing and operates in a distributed way. Information about the similarity between multimedia files (items) is stored locally at these items in so called item-based buddy tables. We propose to use the language model (popular within information retrieval) to build recommendations for the different users based on the buddy tables of those items a user has downloaded previously (indicating the preference of the user). We have tested and compared our distributed collaborative filtering approach to centralized collaborative filtering and showed that it has similar performance. It is therefore a promising technique to facilitate the search for information in peer-to-peer networks. 1
Analysis and Classification of Multi-Criteria Recommender Systems
, 2007
"... Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there exist several contributions describing recommender ..."
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Cited by 20 (3 self)
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Recent studies have indicated that the application of Multi-Criteria Decision Making (MCDM) methods in recommender systems has yet to be systematically explored. This observation partially contradicts with the fact that in related literature, there exist several contributions describing recommender systems that engage some MCDM method. Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation, in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding and development. Towards this direction, this paper identifies a set of dimensions that distinguish, describe and categorize multi-criteria recommender systems, based on existing taxonomies and categorizations. These dimensions are integrated into an overall framework that is used for the analysis and classification of a sample of existing multi-criteria recommender systems. The results provide a comprehensive overview of the ways current multi-criteria recommender systems support the decision of online users.
Preserving privacy in collaborative filtering through distributed aggregation of offline profiles
- In RecSys ’09: Proceedings of the third ACM conference on Recommender systems
, 2009
"... In recommender systems, usually, a central server needs to have access to users ’ profiles in order to generate useful recommendations. Having this access, however, undermines the users ’ privacy. The more information is revealed to the server on the user-item relations, the lower the users ’ privac ..."
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Cited by 19 (2 self)
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In recommender systems, usually, a central server needs to have access to users ’ profiles in order to generate useful recommendations. Having this access, however, undermines the users ’ privacy. The more information is revealed to the server on the user-item relations, the lower the users ’ privacy is. Yet, hiding part of the profiles to increase the privacy comes at the cost of recommendation accuracy or difficulty of implementing the method. In this paper, we propose a distributed mechanism for users to augment their profiles in a way that obfuscates the user-item connection to an untrusted server, with minimum loss on the accuracy of the recommender system. We rely on the central server to generate the recommendations. However, each user stores his profile offline, modifies it by partly merging it with the profile of similar users through direct contact with them, and only then periodically uploads his profile to the server. We propose a metric to measure privacy at the system level, using graph matching concepts. Applying our method to the Netflix prize dataset, we show the effectiveness of the algorithm in solving the tradeoff between privacy and accuracy in recommender systems in an applicable way.
