Using association rules to discover search engines related queries

Fonseca, Bruno M.; Golgher, Paulo B.; Moura, Edleno Silva de; Ziviani, Nívio

doi:10.1109/laweb.2003.1250284

Cited by 84 publications

(91 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…-Fonseca is an alternative to graph-based structures which derives query modification suggestions using association rules [4]. The idea is to use session boundaries and to treat each session as a transaction.…”

Section: Methodsmentioning

confidence: 99%

“…With the increasing availability of search logs obtained from user interactions with search engines, new methods have been developed for mining search logs to capture "collective intelligence" for providing query suggestions. This can be done, for example, by looking at the actual queries submitted and building query flow graphs [1], query-click graphs [2] or association rules [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs

Albakour

Kruschwitz

Nanas

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. User evaluations of search engines are expensive and not easy to replicate. The problem is even more pronounced when assessing adaptive search systems, for example system-generated query modification suggestions that can be derived from past user interactions with a search engine. Automatically predicting the performance of different modification suggestion models before getting the users involved is therefore highly desirable. AutoEval is an evaluation methodology that assesses the quality of query modifications generated by a model using the query logs of past user interactions with the system. We present experimental results of applying this methodology to different adaptive algorithms which suggest that the predicted quality of different algorithms is in line with user assessments. This makes AutoEval a suitable evaluation framework for adaptive interactive search engines.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs

Albakour

Kruschwitz

Nanas

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The two algorithms considered use different approaches for generating recommendations. The first uses association rules [11] (henceforth AssociationRules), while the second exploits click-through data [2] (henceforth CoverGraph). Hereinafter, we will refer to the original formulations of the two algorithms as "static", as opposed to their relative incremental versions which will be called "incremental ".…”

Section: Incremental Algorithms For Query Recommendationmentioning

confidence: 99%

“…Fonseca et al uses association rules as a basis for generating recommendations [11]. The algorithm is based on two main phases.…”

Section: Static Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Incremental Algorithms for Effective and Efficient Query Recommendation

Broccolo

Nardini

Perego

et al. 2010

String Processing and Information Retrieval

View full text Add to dashboard Cite

Abstract. Query recommender systems give users hints on possible interesting queries relative to their information needs. Most query recommenders are based on static knowledge models built on the basis of past user behaviors recorded in query logs. These models should be periodically updated, or rebuilt from scratch, to keep up with the possible variations in the interests of users. We study query recommender algorithms that generate suggestions on the basis of models that are updated continuously, each time a new query is submitted. We extend two stateof-the-art query recommendation algorithms and evaluate the effects of continuous model updates on their effectiveness and efficiency. Tests conducted on an actual query log show that contrasting model aging by continuously updating the recommendation model is a viable and effective solution.

show abstract

Improving search engines by query clustering

Baeza-Yates

Hurtado

Mendoza

2007

J. Am. Soc. Inf. Sci.

View full text Add to dashboard Cite

In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

show abstract

Using association rules to discover search engines related queries

Cited by 84 publications

References 7 publications

AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs

AutoEval: An Evaluation Methodology for Evaluating Query Suggestions Using Query Logs

Incremental Algorithms for Effective and Efficient Query Recommendation

Improving search engines by query clustering

Contact Info

Product

Resources

About