Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T

What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries

We analyze the question queries submitted to a large commercial web search engine to get insights about what people ask, and to better tailor the search results to the users’ needs. Based on a dataset of about one billion question queries submitted during the year 2012, we investigate askers’ querying behavior with the support of automatic query categorization. While the importance of question queries is likely to increase, at present they only make up 3–4% of the total search traffic. Since questions are such a small part of the query stream and are more likely to be unique than shorter queries, clickthrough information is typically rather sparse. Thus, query categorization methods based on the categories of clicked web documents do not work well for questions. As an alternative, we propose a robust question query classification method that uses the labeled questions from a large community question answering platform (CQA) as a training set. The resulting classifier is then transferred to the web search questions. Even though questions on CQA platforms tend to be different to web search questions, our categorization method proves competitive with strong baselines with respect to classification accuracy. To show the scalability of our proposed method we apply the classifiers to about one billion question queries and discuss the trade-offs between performance and accuracy that different classification models offer. Our findings reveal what people ask a search engine and also how this contrasts behavior on a CQA platform

Examining repetition in user search behavior

This paper describes analyses of the repeated use of search engines. It is shown that users commonly re-issue queries, either to examine search results deeply or simply to query again, often days or weeks later. Hourly and weekly periodicities in behavior are observed for both queries and clicks. Navigational queries were found to be repeated differently from others

Understanding Mobile Search Task Relevance and User Behaviour in Context

Improvements in mobile technologies have led to a dramatic change in how and when people access and use information, and is having a profound impact on how users address their daily information needs. Smart phones are rapidly becoming our main method of accessing information and are frequently used to perform `on-the-go' search tasks. As research into information retrieval continues to evolve, evaluating search behaviour in context is relatively new. Previous research has studied the effects of context through either self-reported diary studies or quantitative log analysis; however, neither approach is able to accurately capture context of use at the time of searching. In this study, we aim to gain a better understanding of task relevance and search behaviour via a task-based user study (n=31) employing a bespoke Android app. The app allowed us to accurately capture the user's context when completing tasks at different times of the day over the period of a week. Through analysis of the collected data, we gain a better understanding of how using smart phones on the go impacts search behaviour, search performance and task relevance and whether or not the actual context is an important factor.Comment: To appear in CHIIR 2019 in Glasgow, U

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

Web Usage Mining Architecture and Applications

The WEBMINER is a system that implements parts of this general architecture. The first part is domain dependent application. The second part is the domain independent application. This includes pattern discovery and analysis as part of the system's data mining engine. The overall architecture for the Web mining process is depicted below

Event detection from click-through data via query clustering

The web is an index of real-world events and lot of knowledge can be mined from the web resources and their derivatives. Event detection is one recent research topic triggered from the domain of web data mining with the increasing popularity of search engines. In the visitor-centric approach, the click-through data generated by the web search engines is the start up resource with the intuition: often such data is event-driven. In this thesis, a retrospective algorithm is proposed to detect such real-world events from the click-through data. This approach differs from the existing work as it: (i) considers the click-through data as collaborative query sessions instead of mere web logs and try to understand user behavior (ii) tries to integrate the semantics, structure, and content of queries and pages (iii) aims to achieve the overall objective via Query Clustering. The problem of event detection is transformed into query clustering by generating clusters - hybrid cover graphs; each hybrid cover graph corresponds to a real-world event. The evolutionary pattern for the co-occurrences of query-page pairs in a hybrid cover graph is imposed for the quality purpose over a moving window period. Also, the approach is experimentally evaluated on a commercial search engine\u27s data collected over 3 months with about 20 million web queries and page clicks from 650000 users. The results outperform the most recent work in this domain in terms of number of events detected, F-measures, entropy, recall etc. --Abstract, page iv