Magellan: An adaptive ontology driven “breaking financial news” recommender (original) (raw)
News recommendation has become an increasingly popular area of research for both the academic researcher and the commercial practitioner. Recently, a "semantic approach" to news recommendation has become popular. Semantic news recommendation normally relies upon a manually constructed domain Ontology and a user profile. The current semantic news recommenders have their flaws: 1. constructing and maintaining a manual Ontology is a manually intensive and laborious task, 2. user profiles may inhibit the selection of novel, but interesting stories which are atypical of the user's previous selections and 3. the current approach semantic approaches ignores the "importance" of a story to the monitored domain. This paper describes Magellan (Magalhães), a semantic recommendation system which ranks "breaking financial news" by their "importance" to a monitored domain. In addition Magellan uses an automatically constructed and maintained adaptive Ontology which autonomously discovers new relations and entities whist removing outdated or erroneous information. Magellan applies a "temporal confidence" to each relation and once a relation's "temporal confidence" falls below a preassigned constant it is removed. The ranking of stories is with a "weighted term frequency" (WTF) scheme. The domain Ontology is used to identify relevant terms in candidate stories. A term's weight is determined by its: 1. Centrality and 2. Prestige (which incorporates a relation's temporal confidence). An evaluation of Magellan with an inverted index and a semantic news recommender system with a static Ontology demonstrated a clear advantage for Magellan.
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