Online advertising has become a popular method for companies to market their products and services to potential customers. The methods used by conventional web advertisement systems to decide on which advertisements to display to users in a real-time bidding environment generally do not consider the latent interests of users and as such it is difficult for advertisers to target and acquire new customers with potential interest in the product. Therefore, we proposed the development of a recommender system which could recommend advertisements to users based on their latent interests. In this paper, we outline two experiment studies related to the development of this system. The first study was carried out to examine the effect of using a long and short browsing history acquisition period to train the user model and predict user interests. The results suggested that a longer browsing history acquisition period did not necessarily result in better predictive performance. The second study examined the use of a categorized web browsing history to predict user interest. The results showed the accuracy of the classifiers increased when website categories were used instead of Fully Qualified Domain Names.

We propose a method to clarify the evolution of users' information needs related to a user's interests and actions based upon life events such as "childbirth." First, we extract topic transitions using dynamic topic models from blogs posted by users who have experienced life events. Next, we select the topics by computing the differences in topic probabilities before and after the life event. We evaluated our method based on three life events: "childbirth," "finding employment," and "marriage." Our method selected life event-relevant topics such as "child development," "working life," and "wedding ceremony." We found mothers' information needs such as "how to introduce baby food," employees' information needs such as "preparing an induction programme," and couples' information needs such as "wedding reception planning" in each topic.

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.

Identifying important users from social media has recently attracted much attention in information and knowledge management community. Although researchers have focused on users' knowledge levels on certain topics or influence degrees on other users in social networks, previous works have not studied users' prediction ability on future popularity. In this paper, we propose a novel approach to find important bloggers based on their buzzword prediction ability. We conduct a time-series analysis in the blogosphere considering four factors: post earliness, content similarity, entry frequency and buzzword coverage. We perform preparatory work in categorizing a blogger into knowledgeable categories, identifying past buzzwords, analyzing a buzzword's peak time content and growth period, and finally evaluate a blogger's prediction ability on a buzzword and on a category. Experimental results on real-world blog data consisting of 150 million entries from 11 million bloggers demonstrate that the proposed approach can find prophetic bloggers and outperforms others that do not take temporal features into account.