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Application of semantic in information retrieval is a dynamically developing area. Nowadays, elements of semantic search are used in popular systems such as Microsoft Azure, Abbyy Intelligent Search, Google Search with BERT. Using sematic search, it is possible to obtain documents that contain exact meaning instead of set of words. But Lucene is still one of the most popular libraries for search purpose and it has its own syntax for fuzzy, wildcard, proximity and other modifiers for queries. To evaluate precision and recall of search the authors have created a list of queries and divided it into groups according to a query type. The article contains results of this investigation for semantic search with metagraph knowledge base in comparison to Lucene with the same morphological analyzer. The quantity of documents for two types of search may be the same but ranking should be different. Ranking of queries is another issue and its evaluation is not a trivial task. In this article the authors applied Levenstein distance but then proposed a new method for comparison of ranking given by different search engines. All results were obtained on Open Corpora text corpus.

Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries–in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.

As consumers move through their decision journey, they adopt different goals (e.g., transactional vs. informational). In this research, the authors propose that consumer goals can be detected through textual analysis of online search queries and that both marketers and consumers can benefit when paid search results and advertisements match consumer search–related goals. In bridging construal level theory and textual analysis, the authors show that consumers at different stages of the decision journey tend to assume different levels of mental construal, or mindsets (i.e., abstract vs. concrete). They find evidence of a fluency-driven matching effect in online search such that when consumer mindsets are more abstract (more concrete), consumers generate textual search queries that use more abstract (more concrete) language. Furthermore, they are more likely to click on search engine results and ad content that matches their mindset, thereby experiencing more search satisfaction and perceiving greater goal progress. Six empirical studies, including a pilot study, a survey, three lab experiments, and a field experiment involving over 128,000 ad impressions provide support for this construal matching effect in online search.

This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.

[Display omitted] •Understanding the drivers of organic clicks for search engine optimization (SEO).•Develop a model that provides guidance for SEO practitioners on keyword selection.•Online authority is important at driving organic clicks for informational searches.•Content relevance is important at driving organic clicks for transactional searches. We build an empirical framework using search queries and organic click data which provides model-based guidance to SEO practitioners for keyword selection and web content creation. Specifically, we study how search characteristics (search query popularity, search query competition, search query specificity, and search intent) and website characteristics (content relevance and online authority) interact to affect the expected organic clicks as well as the organic rank a website receives from the search engine result page (SERP). It is often thought that content relevance is a key factor to improve the effectiveness of SEO. We find, however, that content relevance is an important factor in driving organic clicks only when the consumer is farther along in the customer journey and searching for ways to purchase a product. Whereas, when the customer is at the awareness stage and looking for product information, online authority is the key driver of organic clicks.

Firms typically use query-based search to help consumers find information/products on their websites. We consider the problem of optimally ranking a set of results shown in response to a query. We propose a personalized ranking mechanism based on a user’s search and click history. Our machine-learning framework consists of three modules: (a) feature generation, (b) normalized discounted cumulative gain–based LambdaMART algorithm, and (c) feature selection wrapper. We deploy our framework on large-scale data from a leading search engine using Amazon EC2 servers and present results from a series of counterfactual analyses. We find that personalization improves clicks to the top position by 3.5% and reduces the average error in rank of a click by 9.43% over the baseline. Personalization based on short-term history or within-session behavior is shown to be less valuable than long-term or across-session personalization. We find that there is significant heterogeneity in returns to personalization as a function of user history and query type. The quality of personalized results increases monotonically with the length of a user’s history. Queries can be classified based on user intent as transactional, informational, or navigational, and the former two benefit more from personalization. We also find that returns to personalization are negatively correlated with a query’s past average performance. Finally, we demonstrate the scalability of our framework and derive the set of optimal features that maximizes accuracy while minimizing computing time. This paper was accepted by Juanjuan Zhang, marketing.

Online search intermediaries, such as Amazon or Expedia, use rankings (ordered lists) to present third-party sellers’ products to consumers. These rankings decrease consumer search costs and increase the probability of a match with a seller, ultimately increasing consumer welfare. Constructing relevant rankings requires understanding their causal effect on consumer choices. However, this is challenging because rankings are endogenous: consumers pay more attention to highly ranked products, and intermediaries rank the most relevant products at the top. In this paper, I use the first data set with experimental variation in the ranking from a field experiment at Expedia to make three contributions. First, I identify the causal effect of rankings and show that they affect what consumers search, but conditional on search, do not affect purchases. Second, I quantify the effect of rankings using a sequential search model and find an average position effect of $1.92, which is lower than literature estimates obtained without experimental variation. I also use model predictions, data patterns, and a feature of the data set (opaque offers) to show rankings lower search costs, instead of affecting consumer expectations or utility. Finally, I show a utility-based ranking built on this model’s estimates benefits consumers and the search intermediary. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1072 .

We propose a new and direct measure of investor attention using search frequency in Google (Search Volume Index (SVI)). In a sample of Russell 3000 stocks from 2004 to 2008, we find that SVI (1) is correlated with but different from existing proxies of investor attention; (2) captures investor attention in a more timely fashion and (3) likely measures the attention of retail investors. An increase in SVI predicts higher stock prices in the next 2 weeks and an eventual price reversal within the year. It also contributes to the large first-day return and long-run underperformance of IPO stocks.

How fast can a cell locate a specific chromosomal DNA sequence specified by a single-stranded oligonucleotide? To address this question, we investigate the intracellular search processes of the Cas9 protein, which can be programmed by a guide RNA to bind essentially any DNA sequence. This targeting flexibility requires Cas9 to unwind the DNA double helix to test for correct base pairing to the guide RNA. Here we study the search mechanisms of the catalytically inactive Cas9 (dCas9) in living Escherichia coli by combining single-molecule fluorescence microscopy and bulk restriction-protection assays. We find that it takes a single fluorescently labeled dCas9 6 hours to find the correct target sequence, which implies that each potential target is bound for less than 30 milliseconds. Once bound, dCas9 remains associated until replication. To achieve fast targeting, both Cas9 and its guide RNA have to be present at high concentrations.