Search Result Diversification Based on Query Facets

Hu, Sha; Dou, Zhicheng; Wang, Xiaojie; Wen, Ji-Rong

doi:10.1007/s11390-015-1567-5

Cited by 4 publications

(3 citation statements)

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“…Finally, they adopt the clustering approach to generate the subtopics and each cluster denotes one subtopic of the initial query. [3] Paper name: Search Result Diversification Based on Query FacetsIn this paper researchers propose three faceted models which diversify search results based on the faceted subtopics. They again adopt the diversification algorithm which improve the result diversity.…”

Section: Methodsmentioning

confidence: 99%

A Survey on Automatically Mining Facets for Web Queries

Pawar¹,

Lomte²

2017

IJECE

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In this paper, a detailed survey on different facet mining techniques, their advantages and disadvantages is carried out. Facets are any word or phrase which summarize an important aspect about the web query. Researchers proposed different efficient techniques which improves the user's web query search experiences magnificently. Users are happy when they find the relevant information to their query in the top results. The objectives of their research are: (1) To present automated solution to derive the query facets by analyzing the text query; (2) To create taxonomy of query refinement strategies for efficient results; and (3) To personalize search according to user interest. Keyword:Web crawling Indexing QD miner Copyright © 2017Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Duhita Pawar, Departement of Computer Engineering, Savitribai Phule Pune University, Near Jagrut School, Ma Shakti Nagar,Warud Email: duhita.pawar24@gmail.com INTRODUCTIONThere are different ways to help users to better describe their query information need. Query reformulation and query recommendation (or query suggestion) are two popular ways out of them. The main goal of facets mining is different from query recommendation and reformulation. The goal of the facet mining is to summarize the knowledge and information contained in the query. A query facet is a collection of related and informative words which describes important aspects of the query. Here a facet item is typically a word. A Web query has multiple facets that summarize the information about the query from different perspectives. If the user enter 'watches' as query then different aspects of the query 'watches' are displayed as facets which includes gender categories, brands, styles, colors, etc. Facets are assigned weight in order to display the facets priority wise. Techniques used in various approach as seen in Table 1.

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Section: Methodsmentioning

confidence: 99%

A Survey on Automatically Mining Facets for Web Queries

Pawar¹,

Lomte²

2017

IJECE

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“…from which different aspects can be mined. Hu, Dou, Wang, and Wen (2015) extended the latter approach with the aspects derived from an additional data source, namely, the lists appearing in the candidate documents. In contrast to the latter approaches, our experiments focus on dimensions of diversification that are appropriate to an educational search engine.…”

Section: Related Workmentioning

confidence: 99%

Explicit diversification of search results across multiple dimensions for educational search

Yigit-Sert

Altıngövde

Macdonald

et al. 2020

Asso for Info Science & Tech

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Making use of search systems to foster learning is an emerging research trend known as search as learning. Earlier works identified result diversification as a useful technique to support learning‐oriented search, since diversification ensures a comprehensive coverage of various aspects of the queried topic in the result list. Inspired by this finding, first we define a new research problem, multidimensional result diversification, in the context of educational search. We argue that in a search engine for the education domain, it is necessary to diversify results across multiple dimensions, that is, not only for the topical aspects covered by the retrieved documents, but also for other dimensions, such as the type of the document (e.g., text, video, etc.) or its intellectual level (say, for beginners/experts). Second, we propose a framework that extends the probabilistic and supervised diversification methods to take into account the coverage of such multiple dimensions. We demonstrate its effectiveness upon a newly developed test collection based on a real‐life educational search engine. Thorough experiments based on gathered relevance annotations reveal that the proposed framework outperforms the baseline by up to 2.4%. An alternative evaluation utilizing user clicks also yields improvements of up to 2% w.r.t. various metrics.

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“…In this work, we address the issue and improve query facet extraction under the precision-oriented scenario. Other than faceted search, query facet extraction has also be used for diversifying search results [10,9].…”

Section: Related Workmentioning

confidence: 99%

Precision-Oriented Query Facet Extraction

Kong

Allan

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Faceted search has been used successfully for many vertical applications such as e-commerce and digital libraries. However, it remains challenging to extend faceted search to the open-domain web due to the large and heterogeneous nature of the web. Recent work proposed an alternative solution that extracts facets for queries from their web search results, but neglected the precision-oriented perspective of the task-users are likely to care more about precision of presented facets than recall. We improve query facet extraction performance under a precision-oriented scenario from two perspectives. First, we propose an empirical utility maximization approach to learn a probabilistic model by maximizing the expected performance measure instead of likelihood as used in previous approaches. We show that the empirical utility maximization approach can significantly improve over the previous approach under the precision-oriented scenario. Second, instead of showing facets for all queries, we propose a selective method that predicts the extraction performance for each query and selectively shows facets for some of them. We show the selective method can significantly improve the average performance with fair coverage over the whole query set.

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Search Result Diversification Based on Query Facets

Cited by 4 publications

References 35 publications

A Survey on Automatically Mining Facets for Web Queries

A Survey on Automatically Mining Facets for Web Queries

Explicit diversification of search results across multiple dimensions for educational search

Precision-Oriented Query Facet Extraction

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