Adaptive targeting in online advertisement: models based on relative influence of factors

We consider the problem of adaptive targeting for real-time bidding for internet advertisement. This problem involves making fast decisions on whether to show a given ad to a particular user. For demand partners, these decisions are based on information extracted from big data sets containing records of previous impressions, clicks and subsequent purchases. We discuss several criteria which allow us to assess the significance of different factors on probabilities of clicks and conversions. We then devise simple strategies that are based on the use of the most influential factors and compare their performance with strategies that are much more computationally demanding. To make the numerical comparison, we use real data collected by Crimtan in the process of running several recent ad campaigns

ASKyphoplan: a program for deformity planning in ankylosing spondylitis

A closing wedge osteotomy of the lumbar spine may be considered to correct posture and spinal balance in progressive thoracolumbar kyphotic deformity caused by ankylosing spondylitis (AS). Adequate deformity planning is essential for reliable prediction of the effect of surgical correction of the spine on the sagittal balance and horizontal gaze of the patient. The effect of a spinal osteotomy on the horizontal gaze is equal to the osteotomy angle. However, the effect of a spinal osteotomy on the sagittal balance depends on both the correction angle and the level of osteotomy simultaneously. The relation between the correction angle, the level of osteotomy and the sagittal balance of the spine can be expressed by a mathematical equation. However, this mathematical equation is not easily used in daily practice. We present the computer program ASKyphoplan that analyses and visualizes the planning procedure for sagittal plane corrective osteotomies of the spine in AS. The relationship between the planned correction angle, level of osteotomy and sagittal balance are coupled into the program. The steps taken during an ASKyphoplan run are outlined, and the clinical application is discussed. The application of the program is illustrated by the analysis of the data from a patient recently treated by a lumbar osteotomy in AS. The software can be used free of charge on the internet at http://www.stega.nl under the heading “research” in the menu

An Improved Algorithm for Fast K-Word Proximity Search Based on Multi-Component Key Indexes

A search query consists of several words. In a proximity full-text search, we want to find documents that contain these words near each other. This task requires much time when the query consists of high-frequently occurring words. If we cannot avoid this task by excluding high-frequently occurring words from consideration by declaring them as stop words, then we can optimize our solution by introducing additional indexes for faster execution. In a previous work, we discussed how to decrease the search time with multi-component key indexes. We had shown that additional indexes can be used to improve the average query execution time up to 130 times if queries consisted of high-frequently occurring words. In this paper, we present another search algorithm that overcomes some limitations of our previous algorithm and provides even more performance gain. This is a pre-print of a contribution published in Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251, published by Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-55187-2_3

Topic-driven toxicity: Exploring the relationship between online toxicity and news topics

Hateful commenting, also known as 'toxicity', frequently takes place within news stories in social media. Yet, the relationship between toxicity and news topics is poorly understood. To analyze how news topics relate to the toxicity of user comments, we classify topics of 63,886 online news videos of a large news channel using a neural network and topical tags used by journalists to label content. We score 320,246 user comments from those videos for toxicity and compare how the average toxicity of comments varies by topic. Findings show that topics like Racism, Israel-Palestine, and War & Conflict have more toxicity in the comments, and topics such as Science & Technology, Environment & Weather, and Arts & Culture have less toxic commenting. Qualitative analysis reveals five themes: Graphic videos, Humanistic stories, History and historical facts, Media as a manipulator, and Religion. We also observe cases where a typically more toxic topic becomes non-toxic and where a typically less toxic topic becomes "toxicified" when it involves sensitive elements, such as politics and religion. Findings suggest that news comment toxicity can be characterized as topic-driven toxicity that targets topics rather than as vindictive toxicity that targets users or groups. Practical implications suggest that humanistic framing of the news story (i.e., reporting stories through real everyday people) can reduce toxicity in the comments of an otherwise toxic topic

Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data

We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer segments to automatically generate personas, which are fictional but accurate representations of each integrated behavioral and demographic segment. Results show that this approach can accurately identify both behavioral and demographical customer segments using actual online customer data from which we can generate personas representing real groups of people

The changing information environment for nanotechnology: online audiences and content

The shift toward online communication in all realms, from print newspapers to broadcast television, has implications for how the general public consumes information about nanotechnology. The goal of this study is threefold: to investigate who is using online sources for information and news about science and nanotechnology, to examine what the general public is searching for online with regards to nanotechnology, and to analyze what they find in online content of nanotechnology. Using survey data, we find those who report the Internet as their primary source of science and technology news are diverse in age, more knowledgeable about science and nanotechnology, highly educated, male, and more diverse racially than users of other media. In a comparison of demographic data on actual visits by online users to general news and science Web sites, science sites attracted more male, non-white users from the Western region of the United States than news sites did. News sites, on the other hand, attracted those with a slightly higher level of education. Our analysis of published estimates of keyword searches on nanotechnology reveals people are turning to the Internet to search for keyword searches related to the future, health, and applications of nanotechnology. A content analysis of online content reveals health content dominates overall. Comparisons of content in different types of sites—blogs, government, and general sites—are conducted

Proximity Full-Text Search by Means of Additional Indexes with Multi-component Keys: In Pursuit of Optimal Performance

Full-text search engines are important tools for information retrieval. In a proximity full-text search, a document is relevant if it contains query terms near each other, especially if the query terms are frequently occurring words. For each word in a text, we use additional indexes to store information about nearby words that are at distances from the given word of less than or equal to the MaxDistance parameter. We showed that additional indexes with three-component keys can be used to improve the average query execution time by up to 94.7 times if the queries consist of high-frequency occurring words. In this paper, we present a new search algorithm with even more performance gains. We consider several strategies for selecting multi-component key indexes for a specific query and compare these strategies with the optimal strategy. We also present the results of search experiments, which show that three-component key indexes enable much faster searches in comparison with two-component key indexes. This is a pre-print of a contribution "Veretennikov A.B. (2019) Proximity Full-Text Search by Means of Additional Indexes with Multi-component Keys: In Pursuit of Optimal Performance." published in "Manolopoulos Y., Stupnikov S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2018. Communications in Computer and Information Science, vol 1003" published by Springer, Cham. This book constitutes the refereed proceedings of the 20th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2018, held in Moscow, Russia, in October 2018. The 9 revised full papers presented together with three invited papers were carefully reviewed and selected from 54 submissions. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-23584-0_7.Comment: Revised paper of "Veretennikov A.B. Proximity full-text search with a response time guarantee by means of additional indexes with multi-component keys", Selected Papers of the XX International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2018), Moscow, Russia, October 9-12, 2018, http://ceur-ws.org/Vol-2277, http://ceur-ws.org/Vol-2277/paper23.pd

Analysis of Transaction Logs from National Museums Liverpool

The websites of Cultural Heritage institutions attract the full range of users, from professionals to novices, for a variety of tasks. However, many institutions are reporting high bounce rates and therefore seeking ways to better engage users. The analysis of transaction logs can provide insights into users’ searching and navigational behaviours and support engagement strategies. In this paper we present the results from a transaction log analysis of web server logs representing user-system interactions from the seven websites of National Museums Liverpool (NML). In addition, we undertake an exploratory cluster analysis of users to identify potential user groups that emerge from the data. We compare this with previous studies of NML website users