1,721,114 research outputs found
Learning Customer Profiles Using Unlabelled Data
No full textE-commerce sites often recommend products they believe a customer is interested in buying. Many web sites have started to embody recommender systems as a way of personalizing their content for users. This paper presents a recommender system that exploits supervised learning methods to learn user profiles from items previously rated by users. Profiles are used to find, classify, or rank items that are likely to be of interest to the user. A major concern with supervised learning techniques is that they often require a large number of labelled examples to learn accurately. Our proposal to reduce the amount of labelled data required is an algorithm that can learn effectively from a small number of labelled examples augmented with a large number of unlabelled examples. Experiments on a real dataset show that the proposed method is effective
Exploiting supervised learning techniques to model user preferences: personalized e-salespersons for personalized e-stores
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User Profiling to Support Internet Customers: what do you want to buy today?
No full textIn the recent years, the astonishing growth of the Internet and the considerable advances of Web technologies have promoted the development of electronic commerce. While e-commerce has not necessarily allowed businesses to produce more products, it has allowed them to provide consumers with more choices. Instead of tens of thousands of books in a superstore, consumers may choose among millions of books in an online store. Increasing choice has also increased the amount of information that scrupulous customers want process before they are able to select which items meet their needs. One way to address this information overload is the use of personalized systems able to support customers in retrieving information about products they are really interested in. Personalization has become an important strategy in Business-to-Consumer electronic commerce, where a user explicitly wants the e-commerce site to consider his or her own information, such as preferences, in order to improve access to relevant product information. In this paper, we propose a scheme to learn user profiles to support Internet customers. The proposed scheme is designed to handle different levels of users' interests simultaneously. Experimental evaluations show the promise of the approach
A Content-Collaborative Recommender that Exploits WordNet-based User Profiles for Neighborhood Formation
No full textCollaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a naive Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles
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