CiteULike: ssn's query

Use of Temporal Expressions in Web Search

Sérgio Nunes — 2008-03-27T16:23:08-00:00

Advances in Information Retrieval (2008), pp. 580-584.

While trying to understand and characterize users’ behavior online, the temporal dimension has received little attention by the research community. This exploratory study uses two collections of web search queries to investigate the use of temporal information needs. Using state-of-the-art information extraction techniques we identify temporal expressions in these queries. We find that temporal expressions are rarely used (1.5% of queries) and, when used, they are related to current and past events. Also, there are specific topics where the use of temporal expressions is more visible.

Improving weak ad-hoc queries using wikipedia asexternal corpus

Yinghao Li — 2008-01-09T14:11:21-00:00

(2007), pp. 797-798.

Query Log Analysis: Social and Technological Challenges

Craig Murray — 2008-01-09T11:48:46-00:00

ACM SIGIR Forum, Vol. 41, No. 2. (December 2007), pp. 112-120.

A large-scale evaluation and analysis of personalized search strategies

Zhicheng Dou — 2007-05-12T17:41:52-00:00

(2007), pp. 581-590.

Improving personalized web search using result diversification

Filip Radlinski — 2007-11-13T15:33:46-00:00

(2006), pp. 691-692.

Data mining of search engine logs

Martin Whittle — 2007-09-28T09:41:49-00:00

Journal of the American Society for Information Science and Technology, Vol. NA, No. NA. (2007), NA.

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.

Access to Query Logs — An Academic Researcher’s Point of View

Judit Bar-Ilan — 2007-08-09T03:42:15-00:00

(May 2007)

Position Paper. Academic researchers have very limited access to query logs of major web search engines. Studying and analyzing large-scale query logs is essential for advancing Web IR. We propose setting up review boards with clear rules for appropriate conduct, and allowing researchers access to logs within this framework.

A Retrieval Language for Historical Documents

María — 2007-09-26T16:21:07-00:00

(1998), pp. 216-225.

Improving automatic query classification via semi-supervised learning

SM Beitzel — 2007-09-06T16:16:15-00:00

Data Mining, Fifth IEEE International Conference on (27-30 November 2005), pp. 42-49.

Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose Web search systems. Such classification becomes critical if the system is to return results not just from a general Web collection but from topic-specific back-end databases as well. Maintaining sufficient classification recall is very difficult as Web queries are typically short, yielding few features per query. This feature sparseness coupled with the high query volumes typical for a large-scale search service makes manual and supervised learning approaches alone insufficient. We use an application of computational linguistics to develop an approach for mining the vast amount of unlabeled data in Web query logs to improve automatic topical Web query classification. We show that our approach in combination with manual matching and supervised learning allows us to classify a substantially larger proportion of queries than any single technique. We examine the performance of each approach on a real Web query stream and show that our combined method accurately classifies 46% of queries, outperforming the recall of best single approach by nearly 20%, with a 7% improvement in overall effectiveness.

Temporal profiles of queries

Rosie Jones — 2007-07-18T09:33:15-00:00

ACM Trans. Inf. Syst., Vol. 25, No. 3. (July 2007)

Semantic similarity between search engine queries using temporal correlation

Steve Chien — 2006-11-29T14:40:09-00:00

(2005), pp. 2-11.

Defining a session on Web search engines

Bernard Jansen — 2007-02-28T14:05:35-00:00

Journal of the American Society for Information Science and Technology, Vol. 9999, No. 9999. (2007), NA.

Detecting query reformulations within a session by a Web searcher is an important area of research for designing more helpful searching systems and targeting content to particular users. Methods explored by other researchers include both qualitative (i.e., the use of human judges to manually analyze query patterns on usually small samples) and nondeterministic algorithms, typically using large amounts of training data to predict query modification during sessions. In this article, we explore three alternative methods for detection of session boundaries. All three methods are computationally straightforward and therefore easily implemented for detection of session changes. We examine 2,465,145 interactions from 534,507 users of on May 6, 2005. We compare session analysis using (a) Internet Protocol address and cookie; (b) Internet Protocol address, cookie, and a temporal limit on intrasession interactions; and (c) Internet Protocol address, cookie, and query reformulation patterns. Overall, our analysis shows that defining sessions by query reformulation along with Internet Protocol address and cookie provides the best measure, resulting in an 82% increase in the count of sessions. Regardless of the method used, the mean session length was fewer than three queries, and the mean session duration was less than 30 min. Searchers most often modified their query by changing query terms (nearly 23% of all query modifications) rather than adding or deleting terms. Implications are that for measuring searching traffic, unique sessions may be a better indicator than the common metric of unique visitors. This research also sheds light on the more complex aspects of Web searching involving query modifications and may lead to advances in searching tools.