Organizing User Search Histories
Users are increasingly pursuing complex task-oriented goals on the Web, such as making travel arrangements, managing finances or planning purchases. To this end, they usually break down the tasks into a few co-dependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users in their long-term information quests on the Web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user’s historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. In our approach, we go beyond approaches that rely on textual similarity or time thresholds, and we propose a more robust approach that leverages search query logs. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.
However, this is impractical in our scenario for two reasons. First, it may have the undesirable effect of changing a user’s existing query groups, potentially undoing the user’s own manual efforts in organizing her history. Second, it involves a high computational cost, since we would have to repeat a large number of query group similarity computations for every new query.
We need a relevance measure that is robust enough to identify similar query groups beyond the approaches that simply rely on the textual content of queries or time interval between them. Our approach makes use of search logs in order to determine the relevance between query groups more effectively. In fact, the search history of a large number of users contains signals regarding query relevance, such as which queries tend to be issued closely together (query reformulations), and which queries tend to lead to clicks on similar URLs (query clicks). Such signals are user-generated and are likely to be more robust, especially when considered at scale. We suggest measuring the relevance between query groups by exploiting the query logs and the click logs simultaneously.
We study the problem of organizing a user’s search history into a set of query groups in an automated and dynamic fashion. Each query group is a collection of queries by the same user that are relevant to each other around a common informational need. These query groups are dynamically updated as the user issues new queries, and new query groups may be created over time.
Query Relevance and Search logs:
We now develop the machinery to define the query relevance based on Web search logs. Our measure of relevance is aimed at capturing two important properties of relevant queries, namely: (1) queries that frequently appear together as reformulations and (2) queries that have induced the users to click on similar sets of pages. We start our discussion by introducing three search behavior graphs that capture the aforementioned properties. Following that, we show how we can use these graphs to compute query relevance and how we can incorporate the clicks following a user’s query in order to enhance our relevance metric.
Dynamic Query Grouping:
One approach to the identification of query groups is to first treat every query in a user’s history as a singleton query group, and then merge these singleton query groups in an iterative fashion (in a k-means or agglomerative way. However, this is impractical in our scenario for two reasons. First, existing query groups, potentially doing the user’s own manual efforts in organizing her history. Second, it involves a high computational cost, since we would have to repeat a large number of query group similarity computations for every new query.
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