Abstract. Recommender Systems (RS) suggest useful and interesting items to users in order to increase user satisfaction and online conversion rates. They typically exploit explicit or implicit user feedback such as ratings, buying records or clickstream data and apply statistical methods to derive recommendations. This paper focuses on explicitly formulated customer requirements as the sole type of user feedback. Its contribution lies in comparing different techniques such as knowledge-and utility-based methods, collaborative filtering, association rule mining as well as hybrid variants when user models consist solely of explicit customer requirements. We examine how this type of user feedback can be exploited for personalization in e-commerce scenarios. Furthermore, examples of actual online shops are developed where such contextual user information is available, demonstrating how more efficient RS configurations can be implemented. Results indicate that, especially for new users, explicit customer requirements are a useful source of feedback for personalization and hybrid configurations of collaborative and knowledge-based techniques achieve best results.

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A recommender system (RS) supports online users in e-commerce by proposing products that are assumed to be both useful and interesting. Knowledgebased recommendation systems form one branch of these online sales support systems that is particularly relevant for high-involvement product domains like consumer electronics, financial services or tourism. A constraint-based RS is a specific variant of a knowledge-based RS that builds on a CSP formalism for problem representation and solving. This article formalizes the different variants of a constraint-based recommendation problem based on consistency and the empirical part compares the performance of different constraint-based recommendation mechanisms in offline experiments on historical data.

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Collaborative filtering (CF) is currently the most popular technique used in commercial recommender systems. Algorithms of this type derive personalized product propositions for customers by exploiting statistics derived from vast amounts of transaction data. Traditionally, basic CF algorithms have exploited a single category of ratings despite the fact that on many platforms a variety of different forms of user feedback are available for personalization and recommendation. In this paper we explore a collaborative feature-combination algorithm that concurrently exploits multiple aspects of the user model like clickstream data, sales transactions and explicit user requirements to overcome some known shortcomings of CF like the cold-start problem for new users. We validate our contribution by evaluating it against the standard user-to-user CF algorithm using a dataset from a commercial Web shop. Evaluation results indicate considerable improvements in terms of user coverage and accuracy.