Robin D. Burke

Hybrid Recommender Systems: Survey and Experiments

Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.

Hybrid web recommender systems

Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.

Personalized recommendation in social tagging systems using hierarchical clustering

Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other users profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the users current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.

The FindMe approach to assisted browsing

While the explosion of online information has introduced new opportunities for finding and using electronic data, it has also underscored the problem of isolating useful information and making sense of large, multidimensional information spaces. In response to this problem, we have developed an approach to building data tour guides, called FindMe systems. These programs know enough about an information space to help users navigate through it, making sure they not only come away with useful information but also insights into the structure of the information space itself. In these systems, we have combined the idea of instance-based browsing, which involves structuring retrieval around the critiquing of previously retrieved examples, and retrieval strategies, or knowledge-based heuristics for finding relevant information. This article illustrates these techniques with examples of working FindMe systems, and describes the similarities and differences between them. FindMe tour guides help users to select the perfect car, movie, restaurant, stereo or apartment on the World Wide Web.

Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness

Publicly accessible adaptive systems such as collaborative recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may inject biased profiles in an attempt to force a system to “adapt” in a manner advantageous to them. Such attacks may lead to a degradation of user trust in the objectivity and accuracy of the system. Recent research has begun to examine the vulnerabilities and robustness of different collaborative recommendation techniques in the face of “profile injection” attacks. In this article, we outline some of the major issues in building secure recommender systems, concentrating in particular on the modeling of attacks and their impact on various recommendation algorithms. We introduce several new attack models and perform extensive simulation-based evaluations to show which attacks are most successful and practical against common recommendation techniques. Our study shows that both user-based and item-based algorithms are highly vulnerable to specific attack models, but that hybrid algorithms may provide a higher degree of robustness. Using our formal characterization of attack models, we also introduce a novel classification-based approach for detecting attack profiles and evaluate its effectiveness in neutralizing attacks.

Web search personalization with ontological user profiles

Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that users interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the users ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.

Constraint-based recommender systems: technologies and research issues

Recommender systems support users in identifying products and services in e-commerce and other information-rich environments. Recommendation problems have a long history as a successful AI application area, with substantial interest beginning in the mid-1990s, and increasing with the subsequent rise of e-commerce. Recommender systems research long focused on recommending only simple products such as movies or books; constraint-based recommendation now receives increasing attention due to the capability of recommending complex products and services. In this paper, we first introduce a taxonomy of recommendation knowledge sources and algorithmic approaches. We then go on to discuss the most prevalent techniques of constraint-based recommendation and outline open research issues.

Classification features for attack detection in collaborative recommender systems

Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to identify types of attacks and study mechanisms for recognizing and defeating them. In this paper, we propose and study different attributes derived from user profiles for their utility in attack detection. We show that a machine learning classification approach that includes attributes derived from attack models is more successful than more generalized detection algorithms previously studied.

Robustness of collaborative recommendation based on association rule mining

Standard memory-based collaborative filtering algorithms, such as k-nearest neighbor, are quite vulnerable to profile injection attacks. Previous work has shown that some model-based techniques are more robust than k-nn. Model abstraction can inhibit certain aspects of an attack, providing an algorithmic approach to minimizing attack effectiveness. In this paper, we examine the robustness of a recommendation algorithm based on the data mining technique of association rule mining. Our results show that the Apriori algorithm offers large improvement in stability and robustness compared to k-nearest neighbor and other model-based techniques we have studied. Furthermore, our results show that Apriori can achieve comparable recommendation accuracy to k-nn.

Interactive Critiquing forCatalog Navigation in E-Commerce

E-commerce sites can have large, essentiallyunbounded, catalogs. With large catalogs comesincreasing difficulty for buyers in making useof standard search and browsing facilities.Particularly in the case of casual oroccasional buyers and in the case of complexproducts, the gap between a productsspecifications and the buyers understanding ofneed can be hard to bridge. An effectivee-commerce catalog must map user needs toproducts that can fulfill them. This paperdescribes an interactive, incremental,case-based, critiquing approach to solving thisproblem. The approach is interactive andincremental, so it does not require that theuser have a completely specified need at thestart. The system is case-based in that itemphasizes products over features orconstraints, and uses case-based reasoningtechniques for its product retrieval. Finally,the approach is based on the critiquing ofpresented examples, each critique redirectingthe search to home in on appropriate products.