Measuring the Visual Complexities of Web Pages
ACM Transactions on the Web (TWEB)
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Huge amounts of search and browse log data has been accumulated in various search engines. Such massive search/browse log data, on the one hand, provides great opportunities to mine the wisdom of crowds and improve Web search as well as online advertisement. On the other hand, designing effective and efficient algorithms and tools to clean, model, and process large scale log data presents great challenges. In this tutorial, we give a systematic survey on the applications, challenges, fundamental principles and state-of-the-art methods of mining large scale search and browse log data. We start with an introduction of search and browse log data and an overview of various log mining applications. Then, we focus on four popular areas of log mining applications, namely query understanding, document understanding, query-document matching, and user understanding. For each area, we review the major tasks, analyze the challenges, and exemplify several representative solutions. Finally, we discuss several new directions in search/browse log mining. The tutorial slides are available at the authors' homepages after the tutorial is presented.