Knowledge Infusion into Content-based Recommender Systems

Content-based recommender systems try to recommend items similar to those a given user has liked in the past. The basic process consists of matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object (item). Common-sense and domain-specific knowledge may be useful to give some meaning to the content of items, thus helping to generate more informative features than "plain" attributes. The process of learning user profiles could also benefit from the infusion of exogenous knowledge or open source knowledge, with respect to the classical use of endogenous knowledge (extracted from the items themselves). The main contribution of this paper is a proposal for knowledge infusion into content-based recommender systems, which suggests a novel view of this type of systems, mostly oriented to content interpretation by way of the infused knowledge. The idea is to provide the system with the "linguistic" and "cultural" background knowledge that hopefully allows a more accurate content analysis than classic approaches based on words. A set of knowledge sources is modeled to create a memory of linguistic competencies and of more specific world "facts", that can be exploited to reason about content as well as to support the user profiling and recommendation processes. The modeled knowledge sources include a dictionary, Wikipedia, and content generated by users (i.e. tags provided on items), while the core of the reasoning component is a spreading activation algorithm.