Abstract not found

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

Abstract. Recommendation systems are applied to personalize and customize the Web environment. We have developed a recommendation system, termed Yoda, that is designed to support large-scale Web-based applications requiring highly accurate recommendations in real-time. With Yoda, we introduce a hybrid approach thatcombines collaborative ltering (CF) and content-based querying to achieve higher accuracy. Yoda is structured as a tunable model that is trained o-line and employed for real-time recommendation on-line. The on-line process bene ts from an optimized aggregation function with low complexity that allows realtime weighted aggregation of the soft classi cation of active users to prede ned recommendation sets. Leveraging on localized distribution of the recommendable items, the same aggregation function is further optimized for the o-line process to reduce the time complexity of constructing the pre-de ned recommendation sets of the model. To make the o-line process scalable furthermore, we also propose a ltering mechanism, FLSH, that extends the Locality Sensitive Hashing technique by incorporating anovel distance measure that satis es speci c requirements of our application. Our end-to-end experiments show while Yoda's complexity is low and remains constant as the number of users and/or items grow, its accuracy surpasses that of the basic nearest-neighbor method by a wide margin (in most cases more than 100%). 1

In recent years Services Science has been an emerging discipline that aims to promote service innovation and increase service productivity by aligning scientific, management, and engineering perspectives. It emphasizes that service innovation should be able to create value for both services providers and consumers. To realize the core thinking of services science, that is, high value and high productivity, service design has to incorporate many factors into its consideration. Based on the ideas of this new research field, we develop a personalized daily-life activity recommendation service that includes information behavior, business value, and technology architecture as our service design considerations. Our service can be requested under a ubiquitous environment and include users ’ contextual information which is an important factor in information behavior. With regard to IT architecture, we use the service-oriented architecture (SOA) that provides the flexibility and extensiveness of technology as well as permit new innovative services to be easily added.

The ubiquity of Collaborative Filtering systems is evident in the wide variety of domains to which they have been applied successfully. However a major challenge to such systems is the high dimensionality and sparsity of the expressed preferences. Dealing effectively with large user profiles would improve the scalability of the system whereas reducing sparsity would increase the quality of recommendations. Several approaches in this direction have focused on feature selection and feature extraction in order to reduce the data dimension and thus make the recommendation process more scalable. Some of the features extraction techniques are based on extracting content based features. However many such solutions have been handcrafted and thus not guaranteed to work optimally under all data environments. This work explores Evolutionary algorithms based feature extraction techniques where the extracted features may describe user or item profiles. The features constructed/extracted thus are compact, dense and are discriminative. Moreover they have the advantage of requiring no extra information (such as content description) and are adaptive, delivering the optimal feature extraction scheme for the particular dataset. We have performed experiments with the popular MovieLens dataset and have compared the user-based and item-based evolutionary feature extraction schemes with respect to their accuracy. The experiments establish that the evolutionary feature extraction schemes score over traditional algorithms as well as content-based feature extraction schemes. Moreover we find that the item-based evolutionary feature extraction schemes outperform their user-based counterparts under varying parameter values.