Web Search and Browse Log Mining: Challenges, Methods, and Applications

Abstract

Huge amounts of search log data have been accumulated in various search engines. Currently, a commercial search engine receives billions of queries and collects tera-bytes of log data on any single day. Other than search log data, browse logs can be collected by client-side browser plug-ins, which record the browse information if users’ permissions are granted. Such massive amounts of search/browse log data, on the one hand, provide great opportunities to mine the wisdom of crowds and improve search results as well as online advertisement. On the other hand, designing effective and efficient methods to clean, model, and process large scale log data also presents great challenges.

In this tutorial, I will focus on mining search and browse log data for search engines. I will start with an introduction of search and browse log data and an overview of frequently-used data summarization in log mining. I will then elaborate how log mining applications enhance the five major components of a search engine, namely, query understanding, document understanding, query-document matching, user understanding, and monitoring and feedbacks. For each aspect, I will survey the major tasks, fundamental principles, and state-of-the-art methods. Finally, I will discuss the challenges and future trends of log data mining.