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.

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.

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.

Nowadays, users of information systems have inclination to use a central server to decrease data transferring and maintenance costs. Since such a system is not so trustworthy, users' data usually upkeeps encrypted. However, encryption is not a nostrum for security problems and cannot guarantee the data security. In other words, there are some techniques that can endanger security of encrypted data. Majority of existing methods for encrypted data management have some critical defects such as cryptanalysis attacks, encryption/decryption overhead, and inefficient data storing and retrieval. In this paper, at first we propose a prototype model of private key based search on encrypted data. Then we try to improve it significantly to meet security requirements. Our main goal is to offer a practical method of Querying arbitrary words on encrypted data using a minimal trust model. Moreover, we present a model for balancing between performance and security based on user's requirements. In comparison with other methods, Query response time is improved and the probability of statistical deductions is reduced.