With the rapid increase in the use of search engines, the need for developing more effective information retrieval and ranking methods has become critical. One of the key challenges in information retrieval is predicting query performance, which involves estimating how well a search engine can fulfill a user's information need. Accurate prediction of query performance allows search engines to take adaptive actions, such as query reformulation or ranking adjustment, to enhance retrieval effectiveness. Query Performance Prediction (QPP) methods fall into two main categories: pre-retrieval prediction and post-retrieval prediction. Pre-retrieval predictors estimate query difficulty before the retrieval process, relying on linguistic and statistical query features rather than retrieved documents. In contrast, post-retrieval prediction methods assess query performance based on the ranking list and document collection, providing deeper insights into retrieval effectiveness. In this study, we propose a novel unsupervised post-retrieval QPP method that evaluates query performance by analyzing the clustering behavior of retrieved documents. Our method defines five new metrics—CC, DCIC, DCNIC, DCNICR, and CCR— to measure the distribution and coherence of retrieved documents. These metrics help assess query difficulty by capturing how documents group into clusters, identifying outlier documents that do not fit well into clusters, and evaluating the overall structure of retrieved results. By leveraging these metrics, our approach provides a more fine-grained estimation of query performance without requiring human-labeled data. To evaluate the effectiveness of the proposed method, we conduct experiments on three datasets: TREC DL 2019, TREC DL 2020, and DL-Hard. The results demonstrate that our approach improves Spearman's correlation coefficient by 0.009 and 0.163 on the TREC DL 2019 and DL-Hard datasets, respectively. Additionally, it increases Pearson’s correlation coefficient by 0.037 on the TREC DL 2020 dataset compared to state-of-the-art unsupervised QPP methods. These improvements indicate that clustering-based QPP methods can effectively capture query difficulty and retrieval quality without the need for external supervision.

The goal of a query-by-example music information retrieval system is retrieval of the target song corresponding to user-provided example from a particular dataset. The example can be a few second piece recorded from any music source such as TV or even a noisy environment e. g. gym. In this paper, a query-by-example system for music retrieval using genre recognition is proposed whose goal is to show the effect of genre recognition to achieve the accurate and rapid performance in such systems even in presence the background noise. This system includes two basic blocks: genre recognition and matching-retrieval. A binary decision tree performs the genre recognition and matching-retrieval uses two Euclidean and Kullback-Leibler (KL) distances along with a score level based decision fusion. The proposed system is evaluated on the well-known GTZAN dataset (prepared by George Tzanetakis) and by two random groups of pure and noisy queries. The results show the accuracy of 97% and 86% for two pure and noisy query groups, respectively, in retrieval time of 525 ms with Euclidean distance. These values are 97% and 82% in retrieval time of 380 ms with KL distance.

Query expansion as one of query adaptation approaches, improves retrieval effectiveness of information retrieval. Pseudo-relevance feedback (PRF) is a query expansion approach that supposes top-ranked documents are relevant to the query concept, and selects expansion terms from top-ranked documents. However, Existing of irrelevant document in top-ranked documents is possible. Many approaches have been proposed for selecting relevant documents and ignoring irrelevant ones, which use clustering or classification of documents. Important issue in query expansion approaches is using relevant documents for selecting expansion terms. In this paper, we propose clustering of pseudo-relevant documents based on query sensitive similarity, which is efficient for placing similar documents together. Query sensitive similarity obtained good results in document retrieval rather than term-based similarity, is the reason for using in this paper. Clusters are ranked based on inner similarity, and some top ranked ones are selected for query expansion. Then, we extract expansion terms from documents of selected clusters based on Term Frequency- Inverse document frequency (TF-IDF) scoring function. Conducted experiments over Medicine dataset (MED) shows that retrieval results for expanded queries with selected documents from clusters is better than basic retrieval (VSM) and Pseudo-relevance feedback. In addition, the effectiveness of retrieval is raised.

The web size is increasing continuously. The more the Internet is growing; the more tendencies the people have to use the search engines. Moreover, since most of the commercial search engines are based on keyword indexing, there are many records in their result lists that are irrelevant to the user’s information needs. It is shown that for retrieving more relevant and precise results, the following two points should be concerned: First of all, the query (either it is generated by a human or an intelligent agent) should be expressed in an accurate and exact manner. Second, we should empower search engines with the ability to capture the semantic relation between the words and the query context. Hence, different search engine architectures, each of which containing query refinement or semantic understanding components, have been proposed. Each architectural model has its own specific properties; but, most of them focus on only one of the two points mentioned above to improve the overall system efficiency. Moreover, in existing architectures, query refinement components have direct interaction with users which may either take their time or threat their privacy while gathering basic information. In this paper, we proposed an improved architectural model for agent and ontology based search engine which uses domain ontology for semantic understanding and a query refinement subsystem based on fuzzy ontology. This subsystem helps Search Agents to refine their queries, express them in a more precise way and get more relevant results. The simulation result shows that using this query refinement subsystem by Search Agents can improve the system efficiency up to 5.2%.

In a verifiable database scheme (VDB), a client with limited storage resources securely outsources its very large and dynamic database to an untrusted server such that any attempt to tamper with the data, or even any unintentional changes to the data, can be detected by the client with high probability. The latest work in this area has tried to add the secure search feature of single keyword and multiple keywords. In this paper, we intend to add a range query to the features of this database. The scheme presented in this article provides the requirements of a secure search, namely the completeness of the search result, the proof of the empty search result, the lack of additional information leakage and the freshness of the search results, as well as the database with public verifiability. In the proposed scheme, the computational complexity of the client is not changed significantly compared with the previous scheme, but the computational and storage complexity of the server has increased which is justifiable by its rich resources.

Query recommendation is now an inseparable part of web search engines. The goal of query recommendation is to help users find their intended information by suggesting similar queries that better reflect their information needs. The existing approaches often consider the similarity between queries from one aspect (e. g., similarity with respect to query text or search result) and do not take into account different lexical, syntactic and semantic templates exist in relevant queries. In this paper, we propose a novel query recommendation method that uses a comprehensive set of features to find similar queries. We combine query text and search result features with bipartite graph modeling of user clicks to measure the similarity between queries. Our method is composed of two separate offline (training) and online (test) phases. In the offline phase, it employs an efficient k-medoids algorithm to cluster queries with a tolerable processing and memory overhead. In the online phase, we devise a randomized nearest neighbor algorithm for identifying most similar queries with a low response-time. Our evaluation results on two separate datasets from AOL and Parsijoo search engines show the superiority of the proposed method in improving the precision of query recommendation, e. g., by more than 20% in terms of p@10, compared with some well-known algorithms.

Despite the fact that using the services of outsourced data stream servers has extremely been welcomed, but still the problem of obtaining certainty about received results from these servers is one of the basic challenges in enterprises. For outsourcing these services, the user should be assured by a mechanism about the security of communication channels as well as the correct and honest function of the server, because the server may attack the integrity of the results due to economic and malicious reasons. In such attacks, some parts of results are not sent to the user or sent after being modified or delayed. In this article, we have come up with an efficient method for detecting integrity attacks in outsourced data steam systems based on auditing the results of cross computation. In this method, the main data stream has been enciphered by a key and a small part of data has been enciphered by a different key, as a dependant data stream, and sent to the server. The requested query is applied on both streams and the user judges the integrity of results by comparing the results. Our method imposes a little overhead on the user and needs no change in the structure of the server. The probabilistic modeling of the method shows that this method has a high efficiency and the results of the exprimental analysis confirm this very well.

Purpose: This study aims to determine the effect of query expansion on scientific texts retrieval in Persian. Method: The present study was conducted using a quasi-experimental method. The results are obtained by analyzing 40 initial and expanded queries of postgraduate students in the Faculty of Management, University of Tehran. Query expansion was performed manually using primary research results. Findings: Query expansion of Persian scientific texts leads to an increase in the number of related retrieved documents, as well as the comprehensiveness and accuracy of retrieving scientific data in Elmnet search engine, which as a result, improves the overall performance of information retrieval. Results: Nowadays, automatic query expansion is on the agenda of databases. Given that Persian databases are not fully developed, and the existence of specific problems of writing in the Persian language, information literacy training and the method of defining and expressing information requirements and providing them to the information retrieval systems, can have a significant impact on postgraduate students and researchers, to retrieve the required information and save them time and money.