Web advertising recommender system based on estimating users' latent interests

Web advertising is watched with interest as an advertising method employed by companies to introduce their products and services. Web advertising includes listing advertisement, which shows advertisements related to a search keyword, and interest-matching advertising, which shows advertisements relevant to a user's search content and browsing history. However, it is difficult to show effective Web advertising to potential purchasers using the technique based on conventional keyword matching. In this paper, we consider a recommender system for Web advertising based on analysis of the user's potential interests. In particular, we focus on a user model with potential interest for a certain website by analyzing browsing history. We introduce a Web advertising recommender system that is based not only based on keyword matching, but also on reported learning results. In addition, we argue the influence of the period for acquisition of the browsing history, which is taken when the users' model is learned.