Query Recommendation Research Articles

Sequential query prediction based on multi-armed bandits with ensemble of transformer experts and immediate feedback

We study the problem of predicting the next query to be recommended in interactive data exploratory analysis to guide users to correct content. Current query prediction approaches are based on sequence-to-sequence learning, exploiting past interaction data. However, due to the resource-hungry training process, such approaches fail to adapt to immediate user feedback. Immediate feedback is essential and considered as a signal of the user’s intent. We contribute with a novel query prediction ensemble mechanism, which adapts to immediate feedback relying on multi-armed bandits framework. Our mechanism, an extension to the popular Exp3 algorithm, augments Transformer-based language models for query predictions by combining predictions from experts, thus dynamically building a candidate set during exploration. Immediate feedback is leveraged to choose the appropriate prediction in a probabilistic fashion. We provide comprehensive large-scale experimental and comparative assessment using a popular online literature discovery service, which showcases that our mechanism (i) improves the per-round regret substantially against state-of-the-art Transformer-based models and (ii) shows the superiority of causal language modelling over masked language modelling for query recommendations.

Recommendation System for Academic Queries Using Machine Learning

One kind of recommendation system that uses human expertise to provide individualized recommendations is the academic query recommendation system. Because the recommendations made by these systems are based on the knowledge and experience of actual people, they are frequently more reliable and accurate than those made by fully automated systems. The following elements are required to create a recommendation system for academic queries: feature extraction and selection, user interface design, data collecting and preprocessing, and recommendation algorithm development. In order to promote student participation and interaction, the system can also make use of social networks and collaboration tools. An expert recommendation system can help college students become more motivated and engaged, perform better academically, and have better career outcomes. The system can benefit universities as well as. There are numerous courses accessible on e-learning platforms; users must choose the one that best suits their needs. Recommendation systems are crucial in enabling users to make better automated course selections. It suggests that consumers choose the option of their choice according to their preferences. This technology can determine the users' preferences and past usage to determine what they enjoy most. The suggested work might assist students in choosing online courses based on their preferences. Keywords: Recommendation System, Human Expertise, Machine learning

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research.

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath with a demo video at Youtube.

Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design Implications.

An effective and scalable information retrieval (IR) system plays a crucial role in enabling clinicians and researchers to harness the valuable information present in electronic health records. In a previous study, we developed a prototype medical IR system, which incorporated a semantically based query recommendation (SBQR) feature. The system was evaluated empirically and demonstrated high perceived performance by end users. To delve deeper into the factors contributing to this perceived performance, we conducted a follow-up study using query log analysis. One of the primary challenges faced in IR is that users often have limited knowledge regarding their specific information needs. Consequently, an IR system, particularly its user interface, needs to be thoughtfully designed to assist users through the iterative process of refining their queries as they encounter relevant documents during their search. To address these challenges, we incorporated "query recommendation" into our Electronic Medical Record Search Engine (EMERSE), drawing inspiration from the success of similar features in modern IR systems for general purposes. The query log data analyzed in this study were collected during our previous experimental study, where we developed EMERSE with the SBQR feature. We implemented a logging mechanism to capture user query behaviors and the output of the IR system (retrieved documents). In this analysis, we compared the initial query entered by users with the query formulated with the assistance of the SBQR. By examining the results of this comparison, we could examine whether the use of SBQR helped in constructing improved queries that differed from the original ones. Our findings revealed that the first query entered without SBQR and the final query with SBQR assistance were highly similar (Jaccard similarity coefficient=0.77). This suggests that the perceived positive performance of the system was primarily attributed to the automatic query expansion facilitated by the SBQR rather than users manually manipulating their queries. In addition, through entropy analysis, we observed that search results converged in scenarios of moderate difficulty, and the degree of convergence correlated strongly with the perceived system performance. The study demonstrated the potential contribution of the SBQR in shaping participants' positive perceptions of system performance, contingent upon the difficulty of the search scenario. Medical IR systems should therefore consider incorporating an SBQR as a user-controlled option or a semiautomated feature. Future work entails redesigning the experiment in a more controlled manner and conducting multisite studies to demonstrate the effectiveness of EMERSE with SBQR for patient cohort identification. By further exploring and validating these findings, we can enhance the usability and functionality of medical IR systems in real-world settings.

A cooperative co-evolutionary genetic algorithm for query recommendation

A cooperative co-evolutionary genetic algorithm for query recommendation

Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives

Digital archives (DAs) usually store diverse expert-level materials. Nowadays, access to DAs is increasing for non-expert users, However, they might have difficulties formulating appropriate search queries to find the necessary information. In response to this problem, we propose a query log-based query recommendation algorithm that provides expert knowledge to non-expert users, thus supporting their information seeking in DAs. The use case considered is one where after users enter some general queries, they will be recommended semantically similar expert-level queries in the query logs. The proposed modified conditional restricted Boltzmann machines (M-CRBMs) are capable of utilizing the rich metadata in DAs, thereby alleviating the sparsity problem that conventional restricted Boltzmann machines (RBMs) will face. Additionally, compared with other CRBM models, we drop a large number of model weights. In the experiments, the M-CRBMs outperform the conventional RBMs when using appropriate metadata, and we find that the recommendation results are relevant to the metadata fields that are used in M-CRBMs. Through experiments on the Europeana dataset, we also demonstrate the versatility and scalability of our proposed model.

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing. In this paper advanced model of the Logarithmic Directionality Texture Pattern (LDTP) method with a Metaheuristic Pattern Searching (MPS) system was used to estimate the similarity between the query data in the entire database. The overall work was implemented for the application of the data recommendation process. These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period. Also, with the help of a neural network, the relevancies of feature attributes in the database are predicted, and the matching index was sorted to provide the recommended data for given query data. This was achieved by using the Distributional Recurrent Neural Network (DRNN). This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set. The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset. These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric. The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%, 70%, and 80%. The parameters that are considered for performance analysis are Precision, Recall, F1-score and the accuracy of data retrieval, the query recommendation output, and comparison with other state-of-art methods.

Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System

Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System

An Efficient Incremental Query Recommendation based Query Ranking Model

To retrieve the information from web, search area query estimation depending on the minor query logs is selected as an outstanding method. The log data will be developed very quickly. When new data models are developed the existing models should be updated. In this paper to keep the data up to date a new improved query ranking model is implemented. The new data is added to the existing model incrementally. The results shows the incremental query ranking model based techniques are better efficient than the existing query ranking model with better accuracy.

A web content mining application for detecting relevant pages using Jaccard similarity

<span lang="EN-US">The tremendous growth in the availability of enormous text data from a variety of sources creates a slew of concerns and obstacles to discovering meaningful information. This advancement of technology in the digital realm has resulted in the dispersion of texts over millions of web sites. Unstructured texts are densely packed with textual information. The discovery of valuable and intriguing relationships in unstructured texts demands more computer processing. So, text mining has developed into an attractive area of study for obtaining organized and useful data. One of the purposes of this research is to discuss text pre-processing of automobile marketing domains in order to create a structured database. Regular expressions were used to extract data from unstructured vehicle advertisements, resulting in a well-organized database. We manually develop unique rule-based ways of extracting structured data from unstructured web pages. As a result of the information retrieved from these advertisements, a systematic search for certain noteworthy qualities is performed. There are numerous approaches for query recommendation, and it is vital to understand which one should be employed. Additionally, this research attempts to determine the optimal value similarity for query suggestions based on user-supplied parameters by comparing MySQL pattern matching and Jaccard similarity.</span>

Remote OPAC users’ search query reformulation (SQR) patterns: a transaction log analysis

PurposeUsers' search logs are implicit feedbacks on how searchers interact with online information retrieval (IR) systems. The purpose of this paper is to analyze search query reformulation (SQR) patterns of University of Dar es Salaam remote OPAC users.Design/methodology/approachQualitative and quantitative analysis of transaction logs were employed to ascertain the characteristics of search queries and the patterns in which remote OPAC users reformulate their search queries. The study covered a period of six months, commencing from January to June 2019.FindingsA total of 30,474 search hits were submitted by remote OPAC users during the period under study. Individuals from academic and research institutions, computing consortia, and telecommunication companies are the main users of the system. Most of the searches originated from North America and Europe, with few searches coming from China and India. Besides improving search results, SQRs are linked with the existence of multiple information demands as manifested by the use of heterogeneous headwords within individual search episodes.Research limitations/implicationsData collected covered only six months. Similarly, it was however not possible to analyze users' search query formulation within specific contexts such as task-based information searching.Practical implicationsA query recommendation system should be integrated into the OPAC functionalities to improve users' search experiences. Alternatively, there should be a migration to a new system that offers more advanced search features and functionalities.Originality/valueThe study has contributed new insights in SQR studies particularly on how non-institutional affiliated users translate their information needs into search queries during information searching processes.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2020-0389

An efficient framework for the similarity prediction with query recommendation in E‐learning system

An efficient framework for the similarity prediction with query recommendation in E‐learning system

Retracted: QGMS: A query growth model for personalization and diversification of semantic search based on differential ontology semantics using artificial intelligence

AbstractThe inclusion of collective intelligence through a semantic focused affective computing can incorporate intelligence to web search and ensure its compliance with the Web 3.0. In this article, a query growth model with inclusive and exclusive ontology semantics has been proposed for diversification of query recommendation in semantic search. The ontology semantics include query augmented ontology generation, agent‐driven attractor‐distractor generation to yield a merged ontology, and endowment of merged ontology by using hybridization of a series of knowledge bases. The strategy further includes the formulation of a semantic network and entity leveraging based on description logics (DLs) to improve the quality of query recommendation. A novel hierarchical entropy cognitive similarity covariance model has been proposed for yielding the most appropriate recommendable query words. The strategy also encompasses the user‐click information for capturing the current user intents to improve the quality queries recommended in semantic search, and thereby incorporate personalization. Experimentations are conducted for the CHiC dataset and the Spring 2006 Query Log dataset and an average accuracy of 96.27% and 92.01%, respectively, with a very low false discovery rate of 0.06 and 0.1 for the respective datasets.

Retracted] Research on the Construction of Crossborder e‐Commerce Logistics Service System Based on Machine Learning Algorithms

Based on machine learning algorithms, this paper designs a crossborder e‐commerce logistics service system recommendation algorithm. First, we introduce the meaning of query recommendation, analyze the mechanism of e‐commerce platform shopping search, redesign the query recommendation process on this basis, establish a Markov decision process model for the problem, and solve the optimal recommendation strategy through deep machine learning algorithms. Second, we design a simple calculation example, use Python programming through a simulated shopping environment, give the solution process of the optimal recommendation strategy in the whole process, and prove the feasibility of the algorithm. The sentiment synthesis word vector is used as the input data structure of the text, the convolutional neural network model and the recurrent neural network model in machine learning are independently designed and constructed, and a shunt is proposed. The rule (shunt) realizes the operation of judging the data and inputting the two machine learning networks. The shunt fully realizes the combination of the advantages of the local feature characterization of the convolutional neural network and the timing characteristics of the recurrent neural network and achieves a more efficient and accurate electrical system. Finally, through simulation experiments, a series of data processing work such as data outlier cleaning, sliding window construction features of data variables, and training set and test set division are designed to convert regression prediction problems into classification problems to predict commodity demand. At the same time, it also compared the effect of the time series model, random forest model, GBDT, single Xgboost model, and the model used in this topic and analyzed the reasons for this difference and the application of each model.

Query Recommendation System in Social Networks

Recommender Systems are software which provides suggestions to the user according to his or her interest. These suggestions are related to supporting users in making their decisions, for example, what to search, what to buy, what to listen, etc. Recommender systems are very important in online stores where there are a lot of items to buy. These recommender systems help user to find things according to their interest and buy them. There are a lot of techniques proposed for recommendation and used in commercial environments. People are thought to trust suggestions from friends more than those from websites that are similar to them [2]. As a result, it is helpful to feed a recommender system with the friends' ratings. However, social media sharing websites' recommender systems have several difficulties, such as ranking the information from the user's friends as well, finding information from other sources in comparison to the user's immediate friends, and using metadata and context links for suggestion. In this research, an architecture based on profile-based crawling of social media sharing websites is proposed for query recommendation.

Personalized Information Retrieval from Friendship Strength of Social Media Comments

Social networks have become an important venue to express the feelings of their users on a large scale. People are intuitive to use social networks to express their feelings, discuss ideas, and invite folks to take suggestions. Every social media user has a circle of friends. The suggestions of these friends are considered important contributions. Users pay more attention to suggestions provided by their friends or close friends. However, as the content on the Internet increases day by day, user satisfaction decreases at the same rate due to unsatisfactory search results. In this regard, different recommender systems have been developed that recommend friends to add topics and many other things according to the seeker’s interests. The existing system provides a solution for personalized retrieval, but its accuracy is still a problem. In this work, we have proposed a personalized query recommendation system that utilizes Friendship Strength (FS) to recommend queries. For FS calculation, we have used the Facebook dataset comprising of more than 22k records taken from four different accounts. We have developed a ranking algorithm that provides ranking based on FS. Compared with existing systems, the proposed system can provide encouraging results. Key research groups and organizations can use this system for personalized information retrieval.

An efficient hybrid query recommendation using shingling and hashing techniques

Search engines recommend queries to improve the satisfaction level of users by shortening their search task. A proper solution for query recommendation is to analyze the users’ behaviors and mimic the query transition patterns adopted by different users who are succeeded in finding their needed information. In this paper, we propose a novel three-layer query recommendation method which is benefited from a query community graph in the first layer. This graph is generated through clustering similar queries which tend to convey the same meaning. To reduce the overhead of clustering while preserving its performance, we utilize locality-sensitive hashing of k-shingles to represent queries in a space with smaller and fixed dimensions. The second layer is enriched by a query-flow graph which models the transitional patterns made by users inside sessions. The hybrid graph, created by consolidating the query community and query-flow graphs, takes into account the lexical similarity as well as the reformulation diversity to suggest queries. The results of our experiments on data logs of two real search engines show that the proposed method outperforms some well-known algorithms by at least 14% with respect to precision and P@10 parameters.

DT2VIS: A Focus+Context Answer Generation System to Facilitate Visual Exploration of Tabular Data.

The visual analysis dialog system utilizing natural language interface is emerging as a promising data analysis tool. However, previous work mostly focused on accurately understanding the query intention of a user but not on generating answers and inducing explorations. A focus+context answer generation approach, which allows users to obtain insight and contextual information simultaneously, is proposed in this work to address the incomplete user query (i.e., input query cannot reflect all possible intentions of the user). A query recommendation algorithm, which applies the historical query information of a user to recommend a follow-up query, is also designed and implemented to provide an in-depth exploration. These ideas are implemented in a system called DT2VIS. Specific cases of utilizing DT2VIS are also provided to analyze data. Finally, the results show that DT2VIS could help users easily and efficiently reach their analysis goals in a comparative study.

UQSCM-RFD: A query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction

UQSCM-RFD: A query–knowledge interfacing approach for diversified query recommendation in semantic search based on river flow dynamics and dynamic user interaction

Personalized query recommendation using semantic factor model

In the field of query recommendation, the current techniques for semantic analysis technology can't meet the demands of users. In order to meet diverse needs, we improved the LDA model and designed a new query recommendation model based on collaborative filtering-Semantic Factor Model (SFM), which combines text information, user interest information and web source. First, we improved the LDA model from bag-of-word to bag-of-phrase to understand the topics expressed by users' frequently used sentences. The phrase bag model treats phrases as a whole and can capture more accurate query intent. Second, we use collaborative filtering to build an evaluation matrix between user interests and personalized expressions. Third, we designed a new scoring function that can recommend the top n resources to users. Finally, we conduct experiments on the AOL data set. The experimental results show that compared with other latest query recommendation techniques, SFM has higher recommendation quality.