A survey of query result diversification

Zheng, Kaiping; Wang, Hongzhi; Qi, Zhixin; Li, Jianzhong; Gao, Hong

doi:10.1007/s10115-016-0990-4

Cited by 37 publications

(21 citation statements)

References 85 publications

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“…The main approaches to solve the Result Diversification Problem (RDP) are classified into three taxonomy groups: distance-based, novelty-based, and coverage-based methods [Drosou et al 2017;Zheng et al 2017].…”

Section: Diversified Similarity Searchingmentioning

confidence: 99%

“…For instance, methods MaxMin and MaxSum aim at maximizing argmax as the minimal and the maximal sum of div values, respectively. Those methods fit poorly on similarity queries because they process the entire dataset O regardless of the query object o q [Zheng et al 2017]. Therefore, the result set is the same for any query object, which expressively reduces the search semantics.…”

Section: Diversified Similarity Searchingmentioning

confidence: 99%

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J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

Novaes¹,

Santos

Carvalho

et al. 2021

jidm

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Similarity searches can be modeled by means of distances following the Metric Spaces Theory and constitute a fast and explainable query mechanism behind content-based image retrieval (CBIR) tasks. However, classical distance-based queries, e.g., Range and k-Nearest Neighbors, may be unsuitable for exploring large datasets because the retrieved elements are often similar among themselves. Although similarity searching is enriched with the imposition of rules to foster result diversification, the fine-tuning of the diversity query is still an open issue, which is is usually carried out with and a non-optimal expensive computational inspection. This paper introduces J-EDA, a practical workbench implemented in Java that supports the tuning of similarity and diversity search parameters by enabling the automatic and parallel exploration of multiple search settings regarding a user-posed content-based image retrieval task. J-EDA implements a wide variety of classical and diversity-driven search queries, as well as many CBIR settings such as feature extractors for images, distance functions, and relevance feedback techniques. Accordingly, users can define multiple query settings and inspect their performances for spotting the most suitable parameterization for a content-based image retrieval problem at hand. The workbench reports the experimental performances with several internal and external evaluation metrics such as P × R and Mean Average Precision (mAP), which are calculated towards either incremental or batch procedures performed with or without human interaction.

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Section: Diversified Similarity Searchingmentioning

confidence: 99%

Section: Diversified Similarity Searchingmentioning

confidence: 99%

J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

Novaes¹,

Santos

Carvalho

et al. 2021

jidm

View full text Add to dashboard Cite

show abstract

“…It finds its application in many scenarios such as data exploration, Web search, and recommendation systems. According to the surveys [13,39,47], diversification problems can be classified into three categories: (1) content-based (or similaritybased) diversification finds items that are dissimilar to each other;…”

Section: Related Workmentioning

confidence: 99%

“…Query result diversification is an important aspect of user-facing applications, such as data exploration, Web search, and recommendation systems [42,14,11,47]. The need for diversification arises when the system has to limit the number of query results: since human users can visually process limited information, interfaces typically need to limit the data they display on the screen to a few points on a map or a small number of items in a list.…”

Section: Introductionmentioning

confidence: 99%

RC-index

2018

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Query result diversification is widely used in data exploration, Web search, and recommendation systems. The problem of returning diversified query results consists of finding a small subset of valid query answers that are representative and different from one another, usually quantified by a diversity score. Most existing techniques for query diversification first compute all valid query results and then find a diverse subset. These techniques are inefficient when the set of valid query results is large. Other work has proposed efficient solutions for restricted application settings, where results are shared across multiple queries. In this paper, our goal is to support result diversification for general range queries over a single relation. We propose the RC-Index, a novel index structure that achieves efficiency by reducing the number of items that must be retrieved by the database to form a diverse set of the desired size (about 1 second for a dataset of 1 million items). Further, we prove that an RC-Index offers strong approximation guarantees. To the best of our knowledge, this is the first index-based diversification method with a guaranteed approximation ratio for range queries.

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“…is the distance between and . In this paper, we adopt content-based diversity based on edit distance, since the other two kinds (intent based diversity and novelty based diversity) are mainly used for semantic analysis [2]. The definition is as follows.…”

mentioning

confidence: 99%

Similarity Search Combining Query Relaxation and Diversification

Shi

Wang

et al. 2017

Database Systems for Advanced Applications

Self Cite

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We study the similarity search problem which aims to find the similar query results according to a set of given data and a query string. To balance the result number and result quality, we combine query result diversity with query relaxation. Relaxation guarantees the number of the query results, returning more relevant elements to the query if the results are too few, while the diversity tries to reduce the similarity among the returned results. By making a trade-off of similarity and diversity, we improve the user experience. To achieve this goal, we define a novel goal function combining similarity and diversity. Aiming at this goal, we propose three algorithms. Among them, algorithms genGreedy and genCluster perform relaxation first and select part of the candidates to diversify. The third algorithm CB2S splits the dataset into smaller pieces using the clustering algorithm of k-means and processes queries in several small sets to retrieve more diverse results. The balance of similarity and diversity is determined through setting a threshold, which has a default value and can be adjusted according to users' preference. The performance and efficiency of our system are demonstrated through extensive experiments based on various datasets.

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A survey of query result diversification

Cited by 37 publications

References 85 publications

J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

RC-index

Similarity Search Combining Query Relaxation and Diversification

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