Major search engines currently use the history of a user's actions (e.g., queries, clicks) to personalize search results. In this paper, we present a new personalized service...
Correlation mining has gained great success in many application domains for its ability to capture the underlying dependency between objects. However, the research of correlation ...
In this paper, we propose a semi-supervised learning approach for classifying program (bot) generated web search traffic from that of genuine human users. The work is motivated by...
Hongwen Kang, Kuansan Wang, David Soukal, Fritz Be...
In this paper, we formalize the novel concept of Constrained Reverse k-Nearest Neighbor (CRkNN) search on mobile objects (clients) performed at a central server. The CRkNN query c...
In this paper we present our ideas for an Arimaa-playing program (also called a bot) that uses plans and pattern matching to guide a highly selective search. We restrict move gener...