Aging effects on query flow graphs for query suggestion

2009 International Conference on Advanced Information Networking and Applications Workshops, 2009

Web search query logs contain valuable information which can be utilized for personalization and improvement of search engine performance. The aim in this paper 1 is to cluster users based on their interests, and analyze the temporal dynamics of these clusters. In the proposed approach, we first apply clustering techniques to group similar users with respect to their web searches. Anticipating that the small number of query terms used in search queries would not be sufficient to obtain a proper clustering scheme, we extracted the summary content of the clicked web page from the query log. In this way, we enriched the feature set more efficiently than the content crawling. We also provide preliminary survey results to evaluate clusters. Clusters may change with the user flow from one cluster to the other as time passes. This is due to the fact that users' interests may shift over time. We used statistical methods for the analysis of temporal changes in users' interests. As a case study, we experimented on the query logs of a search engine.

2007

Search engines have turned into one of the most important services of the Web that are frequently visited by any user. They assist their users in finding appropriate information. Among the many challenging issues in the design of Web search engines that is mostly related to the design of an adaptive interface is recommending suitable query phrases to the end-users. This has two major benefits: firstly the users can more easily interact with the Web search engine and secondly get hints on what is more apt to look for in cases where they may not have any clue. In this paper, we propose a graph based query recommendation algorithm that sequentially recommends query terms to its users. The most important notion behind the design of the algorithm is that the past behavior of previous users of a search engine is mined and a multi-segmented graph is built. Recommendation is made based on the relative similarity of query terms, their frequency and conceptual closeness in the graph.

Foundations and Trends in Information Retrieval, 2010

Lecture Notes in Computer Science, 2011

The increased availability of large amounts of data about user search behaviour in search engines has triggered a lot of research in recent years. This includes developing machine learning methods to build knowledge structures that could be exploited for a number of tasks such as query recommendation. Query flow graphs are a successful example of these structures, they are generated from the sequence of queries typed in by a user in a search session. In this paper we propose to modify the query flow graph by incorporating clickthrough information from the search logs. Click information provides evidence of the success or failure of the search journey and therefore can be used to enrich the query flow graph to make it more accurate and useful for query recommendation. We propose a method of adjusting the weights on the edges of the query flow graph by incorporating the number of clicked documents after submitting a query. We explore a number of weighting functions for the graph edges using click information. Applying an automated evaluation framework to assess query recommendations allows us to perform automatic and reproducible evaluation experiments. We demonstrate how our modified query flow graph outperforms the standard query flow graph. The experiments are conducted on the search logs of an academic organisation's search engine and validated in a second experiment on the log files of another Web site.

—Web recommender systems predict the needs of web users and provide them with recommendations to personalize their pages. Such systems had been expected to have a bright future, especially in ecommerce and E-learning environments. However, although they have been intensively explored in the Web Mining and Machine learning fields, and there have been some commercialized systems, the quality of the recommendation and the user satisfaction of such systems are still not conclusive. In this paper we proposed a more robust approach that leverages search query logs for automatically identifying query groups for a number of different users and record the query logs and their respective sessions. The system uses query reformulation and click graphs which contain useful information on user behavior when searching online. Such information can be used effectively for the task of organizing user search histories into query groups. The proposed technique finds value in combining with keyword semantic similarity and filtering which applies knowledge gained from these query groups in various applications such as providing query suggestions for web personalization by favoring the ranking of search results.

2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 2013

Commercial web search engines include a query suggestion module so that given a user's keyword query, alternative suggestions are offered and served as a guide to assist the user in formulating queries which capture his/her intended information need in a quick and simple manner. Majority of these modules, however, perform an in-depth analysis of large query logs and thus (i) their suggestions are mostly based on queries frequently posted by users and (ii) their design methodologies cannot be applied to make suggestions on customized search applications for enterprises for which their respective query logs are not large enough or non-existent. To address these design issues, we have developed PQS, a probabilistic query suggestion module. Unlike its counterparts, PQS is not constrained by the existence of query logs, since it solely relies on the availability of user-generated content freely accessible online, such as the Wikipedia.org document collection, and applies simple, yet effective, probabilistic-and information retrieval-based models, i.e., the Multinomial, Bigram Language, and Vector Space Models, to provide useful and diverse query suggestions. Empirical studies conducted using a set of test queries and the feedbacks provided by Mechanical Turk appraisers have verified that PQS makes more useful suggestions than Yahoo! and is almost as good as Google and Bing based on the relatively small difference in performance measures achieved by Google and Bing over PQS.

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.

Proceedings of the 21st international conference companion on World Wide Web - WWW '12 Companion, 2012

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

With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize and represent their information needs using simple queries. Users have to keep modifying their input queries until they get expected results. Therefore, it is often desirable for search engines to give suggestions on related queries to users. Besides, by identifying those related queries, search engines can potentially perform optimizations on their systems, such as query expansion and file indexing. In this work we propose a method that suggests a list of related queries given an initial input query. The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules. Users can utilize the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it also performs reasonably well on less frequent input queries.

Mathematical Problems in Engineering, 2015

Query recommendation is an essential part of modern search engine which aims at helping users find useful information. Existing query recommendation methods all focus on recommending similar queries to the users. However, the main problem of these similarity-based approaches is that even some very similar queries may return few or even no useful search results, while other less similar queries may return more useful search results, especially when the initial query does not reflect user’s search intent correctly. Therefore, we propose recommending high utility queries, that is, useful queries with more relevant documents, rather than similar ones. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. At last, we ...