Improving query suggestion by utilizing user intent

Tsai, Shuo-En; Chen, Yi-Shin; Tsai, Chia-Yu; Tu, Shih-Wei

doi:10.1109/iri.2010.5558971

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Shuo-En Tsai and Yi-Shin Chen [33] refer to session data which implicitly embedded users' consecutive queries as crowd wisdom and proposed a Pattern Recognition Query (PRQs) method, which aims to achieve a query suggestion method. The study applies crowd wisdom in two ways, specialization and association.…”

Section: Adjacency Based or Query Co-occurrence Based Query Suggestionmentioning

confidence: 99%

A Survey on Query Suggestion

Meng¹

2014

IJHIT

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Query suggestion attracts great concern recently. It is crucial for capturing frequently asked questions in question-answering system and most popular topics in search engine. Besides these, is also used in advertising retrieval systems, e-commerce system for advertising push to get more profits. The paper gives a general review of query suggestion methods. On the whole, all the methods can be grouped into two categories: session based methods and click-through based methods. Adjacency based query suggestion, co-occurrence based query suggestion, query-flow graph based query suggestion, clustering based query suggestion, and bipartite graph based query suggestion are presented respectively in detail. Furthermore, how to evaluate the performance of query suggestion is denoted. Finally the important related issues of the area in further research are discussed.

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Section: Adjacency Based or Query Co-occurrence Based Query Suggestionmentioning

confidence: 99%

A Survey on Query Suggestion

Meng¹

2014

IJHIT

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“…People describe the information they need in the form of queries, and the search engine returns matched web objects on the results page. However, analysis of search logs shows the proportion of queries without follow-up click-through data is as high as 42%, which indicates that users are not satisfied with the search results [13]. This could be due to the search engine or the user.…”

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confidence: 99%

Clustering user queries into conceptual space

Lee

Chen

2013

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

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The gap between user search intent and search results is an important issue. Grouping terms according to semantics seems to be a good way of bridging the semantic gap between the user and the search engine. We propose a framework to extract semantic concepts by grouping queries using a clustering technique. To represent and discover the semantics of a query, we utilize a web directory and social annotations (tags). In addition, we build hierarchies among concepts by splitting and merging clusters iteratively. Exploiting expert wisdom from web taxonomy and crowd wisdom from collaborative folksonomy, the experiment results show that our framework is effective. I. INTRODUCTIONWith the enormous increase in the amount of information on the Internet, search engines have become an indispensable tool for people looking for information. People describe the information they need in the form of queries, and the search engine returns matched web objects on the results page. However, analysis of search logs shows the proportion of queries without follow-up click-through data is as high as 42%, which indicates that users are not satisfied with the search results [13]. This could be due to the search engine or the user. These two reasons are discussed below.

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Improving Query Suggestion for Digital Libraries

Chao

Zhang

Xing

2011

2011 IEEE 35th Annual Computer Software and Applications Conference Workshops

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The users of a digital library often have difficulty in formulating query expression that could represent his or her information requirements exactly. Query suggestion can provide some recommended query expression and help the user build a proper expression. Query suggestion for digital libraries requires higher precision ratio and novelty ratio than that of web search engines. Based on case studies, we found that there are four main types of query suggestion within digital library environments, namely spelling suggestion, hot keyword suggestion, personalized suggestion and semantic suggestion. These approaches are, however, hardly to ensure high precision ratio and novelty ratio expected by digital library users to date. The paper proposed an improved query suggestion approach for digital library and its main advantages lie in computing semantic relations, finding hot concepts and ranking candidate concepts. Semantic similarity between user's input and a candidate keyword is calculated by Relative Information Loss (RIL). Hot keywords are indentified by a new algorithm, which involving clicked time, novelty clicked time, result record number, novelty result record. The rank degree of a keyword is evaluated by its RIL and hot degree. Finally, a software component that takes advantage of DBpedia Ontology, Jena, Ajax and SPARQL in align with these new improvements is developed and deployed on a digital library named iDLib. Better user experience with our new query suggestion software component proves the feasibility and efficiency of the improvement.

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Improving query suggestion by utilizing user intent

Cited by 5 publications

References 9 publications

A Survey on Query Suggestion

A Survey on Query Suggestion

Clustering user queries into conceptual space

Improving Query Suggestion for Digital Libraries

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