Similarity Search Combining Query Relaxation and Diversification

TL;DR

This paper introduces a novel approach to similarity search that balances result relevance and diversity by combining query relaxation and diversification, supported by new algorithms and adjustable parameters.

Contribution

It proposes a new goal function and three algorithms that effectively balance similarity and diversity in search results, enhancing user experience.

Findings

Algorithms outperform baseline methods in diversity and relevance.

The approach adapts to user preferences via adjustable thresholds.

Extensive experiments validate efficiency and effectiveness.

Abstract

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.