A probabilistic model for semantic advertising

摘要

Contextual advertising focuses on placing suitable advertisements on web pages. To attract user’s intention, the advertisements should be highly related to the target web page. The most effective way to do contextual advertising is ontology-based matching algorithms. The main problem of such algorithms is the difficulty in constructing and populating the ontology for matching advertisements. In this paper, we propose an automatic construction method for advertisement ontology. The construction method searches related documents from Web, extracts keywords and weights keywords for concepts. The weighted keywords are treated as instances of concepts and used to generate centroid vectors for concepts. In order to weight keywords in a proper way, we raise a formula WebSSR (Super-Subordinate Relation by Web). WebSSR weights words based on the probabilities that they have Specific Relations with the target concept. We compare our formula with LDA, NGD, WebJaccard, WebOverlap, WebDice and WebPMI, and our formula outperforms all of them. Experiment results also show that our method is more effective than five baseline methods: Bayesian, SVM, SLSA, LDA and Paragraph2Vec.