An efficient approach to suggesting topically related web queries using hidden topic model

Keyword-based Web search is a widely used approach for locating information on the Web. However, Web users usually suffer from the difficulties of organizing and formulating appropriate input queries due to the lack of sufficient domain knowledge, which greatly affects the search performance. An effective tool to meet the information needs of a search engine user is to suggest Web queries that are topically related to their initial inquiry. Accurately computing query-to-query similarity scores is a key to improve the quality of these suggestions. Because of the short lengths of queries, traditional pseudo-relevance or implicit-relevance based approaches expand the expression of the queries for the similarity computation. They explicitly use a search engine as a complementary source and directly extract additional features (such as terms or URLs) from the top-listed or clicked search results. In this paper, we propose a novel approach by utilizing the hidden topic as an expandable feature. This has two steps. In the offline model-learning step, a hidden topic model is trained, and for each candidate query, its posterior distribution over the hidden topic space is determined to re-express the query instead of the lexical expression. In the online query suggestion step, after inferring the topic distribution for an input query in a similar way, we then calculate the similarity between candidate queries and the input query in terms of their corresponding topic distributions; and produce a suggestion list of candidate queries based on the similarity scores. Our experimental results on two real data sets show that the hidden topic based suggestion is much more efficient than the traditional term or URL based approach, and is effective in finding topically related queries for suggestion. © 2011 Springer Science+Business Media, LLC

Direct Answers or Brief Informative Suggestions? Performance of Different Types of Search Assistance Tools on Different Types on Search Tasks

While search assistance tools can help users with their search in various ways, would they always be effective for every type of search task? This study explored the different performance between two kinds of search assistance tools on exploratory tasks and comparative tasks. A user study was conducted on an experimental web search interface with the search assistance widget displaying on the right-hand side. Each participant was asked to do exploratory and comparative tasks on each search assistance tool. We collected and analyzed data from participants’ web logs, pre-test and post-task questionnaires, and the semi-structured interviews by the end of the study sessions. The findings suggest the effectiveness of each type of search task is different between the two search assistance tools; the dimension assistance is more helpful in comparative tasks whereas the link-suggesting assistance is more favored by exploratory tasks.Master of Science in Information Scienc