Web Recommender System Implementations in Multiple Flavors : Fast and ( Care-) Free for All

In this paper, we present a systematic framework for a fast and easy implementation and deployment of a recommendation system for one or several Websites, based on any available combination of open source tools that include crawling, indexing, and searching capabilities. The supported recommendation strategies include several popular flavors such as content based filtering (straight forward), collaborative filtering (more complex), rule-based, as well as approaches that deal with meta-content, (non-textual) attributes and ontologies, and other variants that include meta-attributes about the user, such as elaborate user profiles, as well as business strategy rules. The biggest advantage of this approach is that for content-based filtering, it allows client or proxy controlled integration of several websites.

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