We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

In recent years, CP-nets have emerged as a useful tool for supporting preference elicitation, reasoning, and representation. CP-nets capture and support reasoning with qualitative conditional preference statements, statements that are relatively natural for users to express. In this paper, we extend the CP-nets formalism to handle another class of very natural qualitative statements one often uses in expressing preferences in daily life – statements of relative importance of attributes. The resulting formalism, TCP-nets, maintains the spirit of CP-nets, in that it remains focused on using only simple and natural preference statements, uses the ceteris paribus semantics, and utilizes a graphical representation of this information to reason about its consistency and to perform, possibly constrained, optimization using it. The extra expressiveness it provides allows us to better model tradeoffs users would like to make, more faithfully representing their preferences. 1.

As people increasingly rely on interactive decision support systems to choose products and make decisions, building effective interfaces for these systems becomes more and more challenging due to the explosion of on-line information, the initial incomplete user preference and user’s cognitive and emotional limitations of information processing. How to accurately elicit user’s preference thereby becomes the main concern of current decision support systems. This paper is a survey of the typical preference elicitation methods proposed by related research works, starting from the traditional utility function elicitation and analytic hierarchy process methods, to computer aided elicitation approaches which include example critiquing, needs-oriented interaction, comparison matrix, CP-network, preferences clustering & matching and collaborative filtering.

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.

Abstract. In the past conversational recommender systems have adopted a similarity-based approach to recommendation, preferring cases that are similar to some user query or profile. Recent research, however, has indicated the importance of diversity as an additional selection constraint. In this paper we attempt to clarify the role of diversity in conversational recommender systems, highlighting the pitfalls of naively incorporating current diversity-enhancing techniques into existing recommender systems. Moreover, we describe and fully evaluate a powerful new diversityenhancing technique that has the ability to significantly improve the performance of conversational recommender systems across the board. 1

User feedback is vital in many recommender systems to help guide the search for good recommendations. Preference-based feedback (e.g. "Show me more like item A ") is an inherently ambiguous form of feedback with a limited ability to guide the recommendation process, and for this reason it is usually avoided. Nevertheless we believe that certain domains demand the use of preference-based feedback. As such, we describe and evaluate a flexible recommendation strategy that has the potential to improve the performance of case-based recommenders that rely on preference-based feedback. 1

Abstract. This paper describes an approach to product recommendation that combines in a novel way content- and collaborative-based filtering techniques. The system helps the user to specify a query that filters out unwanted products in electronic catalogues (content-based). Moreover, if the query produces too many or no results, the system suggests useful query changes that save the gist of the original request. This process goes on iteratively till a reasonable number of products is selected. Then, the selected products are ranked exploiting a case base of recommendation sessions (collaborative-based). Among the user selected items the system ranks higher items that are similar to those selected by other users in similar sessions (twofold similarity). The approach has been applied to a web travel application and it has been evaluated with real users. The proposed approach: a) reduces dramatically the number of user queries, b) reduces the number of browsed products and c) the selected items are found first on the ranked list. 1

Publicly-accessible adaptive systems such as recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may introduce biased data in an attempt to force the system to "adapt " in a manner advantageous to them. Recent research has begun to examine the vulnerabilities of different recommendation techniques. In this paper, we outline some of the major issues in building secure recommender systems, concentrating in particular on the modeling of attacks.

In this paper we describe the knowledge-based recommender application FSAdvisor (Financial Services Advisor) which assists sales representatives in determining personalized financial service portfolios for their customers. Commercially introduced in 2003, FSAdvisor is licensed to a number of major financial service providers in Austria. It supports the dialog between a sales representative and a customer by guaranteeing the consistency and appropriateness of proposed solutions, identifying additional selling opportunities and by providing intelligent explanations for solutions. In the financial services domain (especially in the retail sector) sales representatives can differ greatly in their expertise and level of knowledge. Therefore financial service providers ask for tools effectively supporting sales representatives in the dialog

The complexity of product assortments offered by online selling platforms makes the selection of appropriate items a challenging task. Customers can differ significantly in their expertise and level of knowledge regarding such product assortments. Consequently, intelligent recommender systems are required which provide personalized dialogues supporting the customer in the product selection process. In this paper we present the domainindependent, knowledge-based recommender environment CWAdvisor which assists users by guaranteeing the consistency and appropriateness of solutions, by identifying additional selling opportunities, and by providing explanations for solutions. Using examples from different application domains, we show how model-based diagnosis, personalization, and intuitive knowledge acquisition techniques support the effective implementation of customer-oriented sales dialogues. In this context, we report our experiences gained in industrial projects and present an evaluation of successfully deployed recommender applications.