Nick Golovin

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Recommendations are crucial for the success of large websites. While there are many ways to determine recommendations, the relative quality of these recommenders depends on many factors and is largely unknown. We present the architecture and implementation of AWESOME (Adaptive website recommendations), a data warehouse-based recommendation system. It allows(More)
We present a new approach to information fusion of web data sources. It is based on peer-to-peer mappings between sources and utilizes correspondences between their instances. Such correspondences are already available between many sources, e.g. in the form of web links, and help combine the information about specific objects and support a high quality data(More)
A large number of Web sites use online recommendations to make Web users interested in their products or content. Since no single recommendation approach is always best it is necessary to effectively combine different recommendation algorithms. This paper describes the architecture of a rule-based recommendation system which combines recommendations from(More)
Web recommendation systems have become a popular means to improve the usability of web sites. This paper describes the architecture of a rulebased recommendation system and presents its evaluation on two real-life applications. The architecture combines recommendations from different algorithms in a recommendation database and applies feedback-based machine(More)
Recently, there has been increased interest in the retrieval and integration of hidden-Web datawith a view to leverage high-quality information available in online databases. Although previous works have addressed many aspects of the actual integration, including matching form schemata and automatically filling out forms, the problem of locating relevant(More)
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