Abstract. We report on a study that was undertaken to better identify users' goals behind web search queries by using click through data. Based on user logs which contain over 80 million queries and corresponding click through data, we found that query type identification benefits from click through data analysis; while anchor text information may not be so useful because it is only accessible for a small part (about 16%) of practical user queries. We also proposed two novel features extracted from click through data and a decision tree based classification algorithm for identifying user queries. Our experimental evaluation shows that this algorithm can correctly identify the goals for about 80% web search queries.

In many cases, rather than a keyword search, people intend to see what is going on through the Internet. Then the integrated comprehensive information on news topics is necessary, which we called news issues, including the background, history, current progress, different opinions and discussions, etc. Traditionally, news issues are manually generated by website editors. It is quite a time-consuming hard work, and hence real-time update is difficult to perform. In this paper, a three-step automatic online algorithm for news issue construction is proposed. The first step is a topic detection process, in which newly appearing stories are clustered into new topic candidates. The second step is a topic tracking process, where those candidates are compared with previous topics, either merged into old ones or generating a new one. In the final step, news issues are constructed by the combination of related topics and updated by the insertion of new topics. An automatic online news issue construction process under practical Web circumstances is simulated to perform news issue construction experiments. F-measure of the best results is either above (topic detection) or close to (topic detection and tracking) 90%. Four news issue construction results are successfully generated in different time granularities: one meets the needs like "what's new", and the other three will answer questions like "what's hot" or "what's going on". Through the proposed algorithm, news issues can be effectively and automatically constructed with real-time update, and lots of human efforts will be released from tedious manual work.

News topics, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. However, it is almost impossible to view all the generated topics, because of the large amount. So it will be helpful if all topics are ranked and the top ones, which are both timely and important, can be viewed with high priority. Generally, topic ranking is determined by two primary factors. One is how frequently and recently a topic is reported by the media; the other is how much attention users pay to it. Both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included. However, inconsistency exists between both factors. In this paper, an automatic online news topic ranking algorithm is proposed based on inconsistency analysis between media focus and user attention. News stories are organized into topics, which are ranked in terms of both media focus and user attention. Experiments performed on practical Web datasets show that the topic ranking result reflects the influence of time, the media and users. The main contributions of this paper are as follows. First, we present the quantitative measure of the inconsistency between media focus and user attention, which provides a basis for topic ranking and an experimental evidence to show that there is a gap between what the media provide and what users view. Second, to the best of our knowledge, it is the first attempt to synthesize the two factors into one algorithm for automatic online topic ranking.

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