User Query Research Articles

Integrated Approach for Providing Data Security Verification Over Encrypted Cloud Data

The cloud has recently attracted a lot of user attention from both small and large businesses, including those in software, BPO, healthcare, schools, colleges, and many other industries. For data storage and access from remote places linked to one another from a central server with the aid of the internet, all of these organisations attempt to adopt this centralised cloud server. Because all data is stored remotely and only occasionally accessed locally, the confidentiality of data is crucial to cloud service providers. As is common knowledge, no cloud service provider currently offers data privacy through encryption and message digests to enable data authorisation. almost all businesses An untrusted user can query data files of interest by sending encrypted keywords as a search query to the cloud server. Try to search the data in a secure manner over encrypted cloud data. In this dishonest cloud environment, the returned query results may occasionally be correct, incorrect, or partial. As everyone is aware, cloud servers today nearly actively withhold some qualified results in order to conserve computational resources and communication overhead. In this research, we presented and analysed a safe, practical, and fine-grained query results verification mechanism, by which the query user, given an encrypted query results set, not only can confirm the integrity of each data file, but also determines the overall number of qualifying data files, which are not returned if the set is not finished before the decryption procedure. This served as our primary inspiration for creating a brand-new secure verification object for encrypted cloud storage. Here, the short signature key is created using the message digest method MD5, which is also used to confirm the data's authenticity. We tested our proposed model in a variety of ways, and the results demonstrate that it is a useful and effective system

An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study.

Literature reviews (LRs) identify, evaluate, and synthesize relevant papers to a particular research question to advance understanding and support decision-making. However, LRs, especially traditional systematic reviews, are slow, resource-intensive, and become outdated quickly. LiteRev is an advanced and enhanced version of an existing automation tool designed to assist researchers in conducting LRs through the implementation of cutting-edge technologies such as natural language processing and machine learning techniques. In this paper, we present a comprehensive explanation of LiteRev's capabilities, its methodology, and an evaluation of its accuracy and efficiency to a manual LR, highlighting the benefits of using LiteRev. Based on the user's query, LiteRev performs an automated search on a wide range of open-access databases and retrieves relevant metadata on the resulting papers, including abstracts or full texts when available. These abstracts (or full texts) are text processed and represented as a term frequency-inverse document frequency matrix. Using dimensionality reduction (pairwise controlled manifold approximation) and clustering (hierarchical density-based spatial clustering of applications with noise) techniques, the corpus is divided into different topics described by a list of the most important keywords. The user can then select one or several topics of interest, enter additional keywords to refine its search, or provide key papers to the research question. Based on these inputs, LiteRev performs a k-nearest neighbor (k-NN) search and suggests a list of potentially interesting papers. By tagging the relevant ones, the user triggers new k-NN searches until no additional paper is suggested for screening. To assess the performance of LiteRev, we ran it in parallel to a manual LR on the burden and care for acute and early HIV infection in sub-Saharan Africa. We assessed the performance of LiteRev using true and false predictive values, recall, and work saved over sampling. LiteRev extracted, processed, and transformed text into a term frequency-inverse document frequency matrix of 631 unique papers from PubMed. The topic modeling module identified 16 topics and highlighted 2 topics of interest to the research question. Based on 18 key papers, the k-NNs module suggested 193 papers for screening out of 613 papers in total (31.5% of the whole corpus) and correctly identified 64 relevant papers out of the 87 papers found by the manual abstract screening (recall rate of 73.6%). Compared to the manual full text screening, LiteRev identified 42 relevant papers out of the 48 papers found manually (recall rate of 87.5%). This represents a total work saved over sampling of 56%. We presented the features and functionalities of LiteRev, an automation tool that uses natural language processing and machine learning methods to streamline and accelerate LRs and support researchers in getting quick and in-depth overviews on any topic of interest.

Aeroallergens as Indicators of the Anthropogenic Triads

The growing interest in the complex issue of climate change, environmental pollution and aeroallergens is reflected in the queries of Internet users in various search engines. Recently, articles have been published on the impact of changes in the traditional land use system on the concentration and species composition of plant pollen and fungal spores. The total effects of the influence of the anthropogenic triad on plant pollen and fungal spores: 1) an increase in the time of the beginning of the growth of plants and fungi and, consequently, the beginning of their production; 2) earlier terms and lengthening of the pollination season; 3) an increase in the concentration and species composition of pollen of allergenic plants and fungal spores in the air of the regions, especially in the urban environment, along vertical zonality in mountainous conditions; 4) modification, deformation, fragmentation of pollen grains; 5) change in allergenic potential: increase in the number of allergens in pollen; 6) transformation of allergens in fungal spores; 7) change in the geospatial distribution of plant pollen and fungal spores, i.e. transportation of whole pollen, its fragments and spores over long distances; 8) emergence in the regions, on a global scale of new species of allergenic plants and phytopathogens; 9) changes in the qualitative and quantitative composition of pollen and aerospores, due to a change in the traditional system of land use, sowing of new cultivated plants and, accordingly, the introduction of new phytopathogens; 10) vegetation on the pollen of bacteria and fungal spores that change the structure of the pollen grain; 11) trigger effect of aeroallergens in the occurrence of allergic diseases in humans; 12) an increase in the number of indeterminate pollen and spore particles in aerobiological studies.

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.

PICOTEES: a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants from Chinese children cohorts

The growth in biomedical data resources has raised potential privacy concerns and risks of genetic information leakage. For instance, exome sequencing aids clinical decisions by comparing data through web services, but it requires significant trust between users and providers. To alleviate privacy concerns, the most commonly used strategy is to anonymize sensitive data. Unfortunately, studies have shown that anonymization is insufficient to protect against reidentification attacks. Recently, privacy-preserving technologies have been applied to preserve application utility while protecting the privacy of biomedical data. We present the PICOTEES framework, a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants (https://birthdefectlab.cn:3000/). PICOTEES enables privacy-preserving queries of the phenotype spectrum for a single variant by utilizing trusted execution environment technology, which can protect the privacy of the user's query information, backend models, and data, as well as the final results. We demonstrate the utility and performance of PICOTEES by exploring a bioinformatics dataset. The dataset is from a cohort containing 20,909 genetic testing patients with 3,152,508 variants from the Children's Hospital of Fudan University in China, dominated by the Chinese Han population (>99.9%). Our query results yield a large number of unreported diagnostic variants and previously reported pathogenicity.

Searchable encryption algorithm in computer big data processing application

With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.

Ecoploring the green gems

Supplementary materials Services Marketing: People Technology Strategy (Eighth Edition) By Pearson, Authored by Wirtz Jochen, Lovelock Christopher, Chatterjee Jayanta, 2017; Service-Marketing: Text and Cases (Second Edition) By Pearson, Authored by Harsh Verma, 2011. Social implications Students will gain an understanding of business models, which has a positive impact on the environment. Learning outcomes This case study intends to help learners understand the concept of segmentation, targeting and positioning (STP) in the case of services. The idea of services is different from that of a product as in services; intangible elements dominate the value creation. By exploring the practical scenarios faced by the protagonist, in this case, readers would enhance their problem-solving abilities in similar situations. Case overview/synopsis Prerna is the founder of Ecoplore, an aggregator cum booking platform for eco-hotels. It onboards hotels only if they are made up of vernacular architecture and maintains at least 33% of greenery on its premises. Listed properties also harvested rainwater, used renewable source of energy, used sustainable options instead of single use plastic, composted organic waste. Most of these properties grow vegetables, fruits and herbs, and the visitors enjoy homemade organic meals. Ecoplore is the only aggregator platform to visit and verify these eco-hotels directly. Ecoplore is an early-stage startup; all customer queries were handled by Prerna, on-call or through a chat box. It included interacting with prospective clients, understanding their requirements, suggesting the most suited options and booking the eco-hotels. Prerna had to spend most of the time interacting with clients. Over time, she comprehended that only a few prospective clients became Ecoplore’s customers, and the conversion rate was low of owing to various reasons like ticket size, clients' age and psychology. Complexity academic level For students: This case is aptly designed for marketing students pursuing Bachelor’s in Business Administration and Master of Business Administration courses to understand and apply STP theory and service marketing concepts. Subject code CCS 3: Entrepreneurship.

Context based diversification on keyword search by conceptualization of typed terms of the query

The human era is evolved and dominated through the ultimate intention to know about the Universe and its assets more and more. This in turn persuaded him to gather the immense information of need in the form of theory, tools, intuitions, visuals, and ultimately as the form of abstract objects. In modern Information practice, the art of Conceptualization and the induction of the data driven paradigm identifies the user query intention over a Conceptual network to mold a confident search segment of user choice. The proposed Conceptualization with Typed Terms of Query (CTTQ) approach derives a confident segment of the user query which is contextually measured by means of novel randomized Machine learning algorithms and its surrogates. The Normalized Discounted Cumulative Gain (nDCG) measures of the CTTQ show the usefulness of the query suggestions. It's efficiency improvements are represented with varying impact lexical feature indicator (θ) values.

A deep learning model for natural language querying in Cyber–Physical Systems

As a result of technological advancements, the number of IoT devices and services is rapidly increasing. Due to the increasing complexity of IoT devices and the various ways they can operate and communicate, finding a specific device can be challenging because of the complex tasks they can perform. To help find devices in a timely and efficient manner, in environments where the user may not know what devices are available or how to access them, we propose a recommender system using deep learning for matching user queries in the form of a natural language sentence with Web of Things (WoT) devices or services. The Transformer, a recent attention-based algorithm that gets superior results for natural language problems, is used for the deep learning model. Our study shows that the Transformer can be a recommendation tool for finding relevant WoT devices in Cyber–Physical Systems (CPSs). With hashing as an encoding technique, the proposed model returns the relevant devices with a high grade of confidence. After experimentation, the proposed model is validated by comparing it with our current search system, and the results are discussed. The work can potentially impact real-world application scenarios when many different devices are involved.

Tourism Destination Article Search Features using TF-IDF and Cosine similarity

In the current digital era, the increasing public interest in searching for information about travel destinations necessitates an effective and accurate search system. However, search results for travel destination articles often yield irrelevant or inadequate outcomes. To address this issue, this paper proposes applying the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and Cosine Similarity in the search feature for travel destination articles. By employing these algorithms, the search system is anticipated to deliver more relevant and accurate results according to user needs.
 This research contributes to developing an effective search system for travel destination articles, assisting users in obtaining relevant and high-quality information about the destinations they are searching for. The methodology involves collecting data on travel destination articles, implementing the TF-IDF algorithm to evaluate term importance, and utilizing Cosine Similarity to measure the similarity between articles and user queries.
 The study results demonstrate that implementing the TF-IDF algorithm and Cosine Similarity in the search feature for travel destination articles enhances the accuracy and relevance of search results. Users can quickly discover articles that align with their queries, improving their search experience. In conclusion, this research highlights that applying the TF-IDF algorithm and Cosine Similarity in the search feature significantly improves the accuracy and relevance of search results for travel destination articles. This enhances the search experience for users seeking information about travel destinations.

A rule-based obfuscating focused crawler in the audio retrieval domain

The detection of violations of intellectual properties on multimedia files is a critical problem for the current infrastructure of the Internet, especially within very large document collections. To contrast such a problem, either proactive or reactive methods are used. The first category prevents the upload of infringing files themselves by comparing illegal files with a reference collection, while the second one responds to reports made by third parties or artificial intelligence systems in order to delete files deemed illegal. In this article we propose an approach that is both reactive and proactive at the same time, with the aim of preventing the deletion of legal uploads of files (or modifications of such files, such as remixes, parodies and other edits) due to the presence of illegal uploads on a platform. We developed a rule-based obfuscating focused crawler able to work with audio files in the Audio Information Retrieval (AIR) domain, but its use can be easily extended to other multimedia file types, such as videos or textual documents. Our proposed model automatically scans multimedia files uploaded to the public collection only when a user query is submitted to it. We will also show experimental results obtained during tests on a known musical collection. Several combinations of specific Neural Network-Similarity Scorer solutions are shown, and we will discuss the strength and efficiency of each combination.

Toward Optimal Tradeoff Between Data Freshness and Update Cost in Information-Update Systems

In this paper, we consider a discrete-time information-update system, where a service provider can proactively retrieve information from the information source to update its data and users query the data at the service provider. One example is crowdsensing-based applications. In order to keep users satisfied, the application desires to provide users with fresh data, where the freshness is measured by the Age-of-Information (AoI). However, maintaining fresh data requires the application to update its database frequently, which incurs an update cost (e.g., incentive payment). Hence, there exists a natural tradeoff between the AoI and the update cost at the service provider who needs to make update decisions. To capture this tradeoff, we formulate an optimization problem with the objective of minimizing the total cost, which is the sum of the staleness cost (which is a function of the AoI) and the update cost. Then, we provide two useful guidelines for the design of efficient update policies. Following these guidelines and assuming that the aggregated request arrival process is Bernoulli, we prove that there exists a threshold-based policy that is optimal among all online policies and thus focus on the class of threshold-based policies. Furthermore, we derive the closed-form formula for computing the long-term average cost under any threshold-based policy and obtain the optimal threshold. Finally, we perform extensive simulations using both synthetic data and real traces to verify our theoretical results and demonstrate the superior performance of the optimal threshold-based policy compared with several baseline policies.

Dynamics of interest in higher education before and during ongoing war: Google Trends Analysis

This paper explores how the war in Ukraine changed the interest in higher education of Ukrainians who stayed on the territory of Ukraine and emigrated to other countries. The methodology is based on Google Trends Analysis and peak approach with Google Trends Scale of Internet user inquiries about higher education from June 20, 2021 to June 20, 2023 with a middle point on February 24, 2022. Dynamics of changes in the queries of Internet users by keywords regarding studied higher education are: 1) exclusively from the territory of Ukraine; 2) from the territory of Poland, Slovakia, Germany, the Czech Republic, Great Britain, Spain, Italy, Bulgaria, Romania, Moldova, Austria, i.e., top-10 countries by number of registered Ukrainian refugees according to the UN Refugee Agency. The key results are: 1) increased interest of Internet users in higher education after beginning of the full-scale war: Poland – 22.9%, Romania – 28.9%, Ukraine – 31.2%, Hungary – 32.4%, Slovakia – 35.8%, Moldova – 49.0% of average number of «university» inquiries; 2) increased requests for professional education (42.2%), distance education (25.6%), distance learning (34.1%) after February 24, 2022; 3) correlation between negative trends of interest per 32% from July 2021 (100 GT Scale) to July 2022 (68 GT Scale) in Ukraine and positive trends of this indicator in European counties in August 2022 (80-100 GT Scale). Chi-square test showed statistical significance of changes in interest in higher education (p-value = 0). Key findings demonstrate the following trends after February 24, 2022: distance learning development, increased Internet users’ orientation towards professional education for high-paying jobs, popularity of flexible schedules. Acknowledgments The educational outcomes in this publication were created with the support of the EU Erasmus+ program within the framework of projects ERASMUS-JMO-2021-HEI-TCH-RSCH-101048055 – «AICE – With Academic integrity to EU values: step by step to common Europe» and ERASMUS-JMO-2022-HEI-TCH-RSCH-101085198 «OSEE – Open Science and Education in Europe: success stories for Ukrainian academia».

An Efficient Data Structure Providing Maps of the Frequency of Public Transit Service Within User-Specified Time Windows

Abstract. To understand the quality of a public transportation network, map-based visualizations are the first choice. Since the network quality depends on the transportation schedule, the visualization interface should incorporate time. In this work, we provide such an interface where users can filter the data for time windows. When a user queries for a certain time window, we display all network segments that are traversed by at least θ means of public transportation, e.g., buses in the time window on the map. This gives insights into the frequency of the public transit service.Since such transportation networks can contain a high number of network segments, testing each network segment at the moment when the user specifies the time-window query is not fast enough. We investigate an approach to provide the query result in real time. Our contribution is a data structure that answers such arbitrary time-window queries from the user interface. The data structure is based on a tree structure which is augmented with further information. To evaluate our data structure, we perform experiments on real-world data. With our data structure, we answer time-window queries in at most 25 milliseconds whereas testing each network segment on-demand takes at least 60 milliseconds for the data set that has 102599 road segments.

METODE VECTOR SPACE MODEL UNTUK WEB SCRAPING PADA WEBSITE FREELANCE

Abstract— In digitalization era, internet is at the center of all lines of community activity, just like the field of work. Currently, many platforms provide job vacancies, especially for freelancers. To obtain this information, users usually need to open several websites to find information about suitable job vacancies. Web scraping offers solution to overcome these problems. Based on research that has been done, the BeautifulSoup and Selenium libraries will be used to collect data. To search for data, vector space model method is used to find the level of data similarity between the query and the document. In exploring data, the average near-perfect recall value is 98%, while the average precision value is 56%. This is because data search uses three parameters, so the possibility of retrieving irrelevant data is more significant if the document contains a word in the user's query, even though the context does not match. Utilizing the Streamlit framework in Python can display the data processing results and help users navigate the web scraping process, data processing, and data search. This study aims to implement the web scraping method to retrieve data from freelance websites: Freelance, Project, and Sribulancer. By applying the vector space model method, users can search data from several websites without opening freelance websites one by one. Using data visualization in the form of a web application using the Streamlit framework, the web scraping results can also be processed to be presented in a more helpful form and save the user's time

KGNav: A Knowledge Graph Navigational Visual Query System

Visual query is a vital technique for comprehending and analyzing knowledge graphs, which provides an effective method to lower the barrier of querying knowledge graphs for non-professional users. Nevertheless, visual query techniques for knowledge graphs and ontologies that have emerged in recent years cannot bridge the gap between global information provided by the knowledge graph schema and underlying data of knowledge graph. Thus it cannot fully exploit the global information to navigate users for querying knowledge graphs. This demonstration showcases KGNav, a Knowledge Graph Navigational visual query system. KGNav (1) redefines the minimal unit of operation to abstract the conceptual hierarchy, i.e., Knowledge Graph Schema, in the domain from the original knowledge graph in an offline semi-automatic way through the equivalence relations between these units; it also (2) provides a series of operators and an interactive GUI to capture user query intentions, guiding users to explore the Knowledge Graph Schema to achieve in-depth analysis of knowledge graphs. We will demonstrate the capability of KGNav in reducing tedious queries, enabling users to swiftly grasp the structure of the knowledge graph, and performing queries through several fundamental scenarios.

Information Retrieval Performance in Text Generation using Knowledge from Generative Pre-trained Transformer (GPT-3)

The rise of advanced language models like GPT-3 and text generation has witnessed remarkable progress. However, leveraging the vast amount of knowledge within these models to enhance information retrieval performance remains an area that needs to be explored. This research used Artificial Intelligence, specifically the OpenAI GPT-3 language model, to create an application to help make written content. This research investigates the impact of incorporating GPT-3's knowledge into text generation processes and evaluates its influence on information retrieval tasks. Several features in text generation generate text that requires exact information, such as specifications for a product and accurate descriptions of a job or product, which are included in the concept of information retrieval in text creation by language models. The research used the few-shot learning method in the GPT-3 language model. The generated responses are then evaluated using established information retrieval metrics such as precision, recall, and F1-score. The findings of this research reveal the effectiveness of utilizing GPT-3's knowledge in enhancing information retrieval performance. The generated responses demonstrate improved relevance to user queries, resulting in the same performance precision and recall scores compared to other paid text generator websites. Application results are testing in capabilities of retrieving some information. Application capabilities tested on other commercial text generator engines. The test results obtained BERTscore 86\% (precision), 88\% (recall), and 87\% (F1-Score).

Clustering and Retrieval of News articles using Natural Language Processing

This study focused on organizing and finding news articles from a large dataset using Natural Language Processing (NLP). In journalism, it's important to manage information effectively due to the increasing amount of data available. Clustering groups similar articles together, while retrieval helps find specific articles based on certain criteria. These techniques help journalists and researchers identify patterns and extract useful information. The study used the Reuters dataset, which contains a lot of news stories. Different clustering algorithms were tested, and one called Agglomerative Clustering performed the best. For retrieval, a system was created that finds articles like user queries using cosine similarity. The study had some limitations, such as unclear categorization and a complex dataset. Future work could involve trying other clustering algorithms, considering additional features like author and publication date, and using a more diverse dataset. Key Words: Machine Learning, Retrieval, Reuters Dataset, News Articles

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a multiuser incomplete preference k-nearest neighbor query algorithm based on differential privacy in the road network. The algorithm is divided into four parts; the first part proposes a multiuser incomplete preference completion algorithm based on association rules. The algorithm firstly uses the frequent pattern tree proposed in this paper to mine frequent item sets, then uses frequent item sets to mine strong correlation rules, and finally completes multiuser incomplete preference based on strong correlation rules. The second part proposes attribute preference weight coefficient based on multiuser’ s different preferences and clusters users accordingly. The third part compares the dominance of the query object, filters the data with low dominance, and performs a k-neighbor query. The fourth part proposes a privacy budget allocation method based on differential privacy technology. The method uses the Laplace mechanism to add noise to the result release and balance the privacy and availability of data. Theoretical research and experimental analysis show that the proposed method can better deal with the multiuser incomplete preference k-nearest neighbor query and privacy protection problems in the road network.

Reverse Maximum Inner Product Search: Formulation, Algorithms, and Analysis

The maximum inner product search (MIPS), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. Usually e-commerce companies face situations where they want to promote and sell new or discounted items. In these situations, we have to consider the following questions: Who is interested in the items, and how do we find them? This article answers this question by addressing a new problem called reverse maximum inner product search (reverse MIPS). Given a query vector and two sets of vectors (user vectors and item vectors), the problem of reverse MIPS finds a set of user vectors whose inner product with the query vector is the maximum among the query and item vectors. Although the importance of this problem is clear, its straightforward implementation incurs a computationally expensive cost. We therefore propose Simpfer, a simple, fast, and exact algorithm for reverse MIPS. In an offline phase, Simpfer builds a simple index that maintains a lower bound of the maximum inner product. By exploiting this index, Simpfer judges whether the query vector can have the maximum inner product or not, for a given user vector, in a constant time. Our index enables filtering user vectors, which cannot have the maximum inner product with the query vector, in a batch. We theoretically demonstrate that Simpfer outperforms baselines employing state-of-the-art MIPS techniques. In addition, we answer two new research questions. Can approximation algorithms further improve reverse MIPS processing? Is there an exact algorithm that is faster than Simpfer? For the former, we show that approximation with quality guarantee provides a little speed-up. For the latter, we propose Simpfer++, a theoretically and practically faster algorithm than Simpfer. Our extensive experiments on real datasets show that Simpfer is at least two orders of magnitude faster than the baselines, and Simpfer++ further improves the online processing time.