Query Log Analysis for Enhancing Web Search

Foundations and Trends in Information Retrieval, 2010

Journal of the American Society for Information Science and Technology, 2005

A large number of studies have investigated the transaction log of general-purpose search engines such as Excite and AltaVista, but few studies have reported on the analysis of search logs for search engines that are limited to particular Web sites, namely, Web site search engines. In this article, we report our research on analyzing the search logs of the search engine of the Utah state government Web site. Our results show that some statistics, such as the number of search terms per query, of Web users are the same for general-purpose search engines and Web site search engines, but others, such as the search topics and the terms used, are considerably different. Possible reasons for the differences include the focused domain of Web site search engines and users' different information needs. The findings are useful for Web site developers to improve the performance of their services provided on the Web and for researchers to conduct further research in this area. The analysis also can be applied in e-government research by investigating how information should be delivered to users in government Web sites.

ACM SIGIR Forum, 1999

In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be ...

Advances in Information Retrieval, 2009

Web Search Engines (WSEs) have stored in their query logs information about users since they started to operate. This information often serves many purposes. The primary focus of this tutorial is to introduce to the discipline of query log mining. We will show its foundations, by giving a unified view on the literature on query log analysis, and also present in detail the basic algorithms and techniques that could be used to extract useful knowledge from this (potentially) infinite source of information. Finally, we will discuss how the extracted knowledge can be exploited to improve different quality features of a WSE system, mainly its effectiveness and efficiency.

2007

抄録: The understanding of how users use search queries is an important step towards developing successful web search engines. Mining search query log is a way to gain insight into user behavior. One technique used to analyze the query log is query clustering. Query clustering can be used to group semantically related queries, and can be used to gain an understanding of query usage. In this work, we focus on queries written in Thai language. A technique for query clustering is proposed.

Journal of the American Society for Information Science and Technology, 2007

This article reports on the development of a novel method for the analysis of Web logs. The method uses techniques that look for similarities between queries and identify sequences of "query transformation". It allows sequences of query transformations to be represented as graphical networks, thereby giving a richer view of search behavior than is possible with the usual sequential descriptions. We also perform a basic analysis to study the correlations between observed transformation codes, with results that appear to show evidence of behavior habits. The method was developed using transaction logs from the Excite search engine to provide a tool for an ongoing research project that is endeavoring to develop a greater understanding of Web-based searching by the general public.

2014

User search behaviour was conceptualized as a strategy undertaken by the user in searching for information. Typically, searching activity on the web involved several steps; query formulation and re formulation, browsing the search results, and search results evaluation. The scope of this study has limited itself to query formulation that reflects the user search behaviour. The proposed method has been shown to successfully identify and classify user behaviour into two components namely; breadth search query and depth search query. The queries were initially recorded into search log through search interface. The search interface is one of the innovative tools that interface the Google search engine. Through this interface, user can enter the query and obtain the search results. In addition, the queries are also recorded for further analysis.

Proceedings ACM SIGKDD Workshop on Web Mining and Web Usage Analysis, WEBKDD 2006, Philadelphia, USA., 2006

In this paper we process and analyze web search engine query and click data from the perspective of the query session (query + clicked results) conducted by the user. We initially state some hypotheses for possible user types and quality profiles for the user session, based on descriptive variables of the session. The query dataset is preprocessed and analyzed using some traditional statistical methods, and then processed by the Kohonen SOM clustering technique, which we use to produce a two level clustering. The clusters are interpreted in terms of the user type and quality profiles defined initially. Then we apply the C4.5 rule induction algorithm to predict the session quality and the user type, using two month’s of click data for training, and testing on data captured during a third consecutive month. The objective of the work is to apply a systematic data mining process to click data, contrasting nonsupervised (Kohonen) and supervised (C4.5) methods to cluster and model the data, in order to identify profiles and rules which relate to theoretical user behavior and user session “quality”.

IFOAM , 2003

This essay was a keynote address given at the International Federation of Organic Agriculture Movements (IFOAM)’s conference, Mainstreaming Organic Trade: New Frontiers, Opportunities and Responsibilities, Bangkok, November 2003. The participants were mainly drawn from Japan, Thailand, Germany and New York State. The recent World Trade Organization COP27 conference in Cancun reminded me of papers I wrote soon after 2000. Contents: 1. Organic trade? 2. The political economy of food in an unequal world; 3. Community currencies as a means of achieving local economic democracy; 4. Is an international green currency possible? -