Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Developing Of The Related Data Search Lsa-based Algorithm And Its Programmed Realization

In this article let\u27s consider the theoretical basis of the data search in large data ordered arrays based on the context of the search request and tracking of semantic relationships. Also the first steps towards the practical implementation of this task are proposed. Simple program to check author\u27s thoughts has been developed. All the researches have been made with the VK social network. Internal API VK was used as retrieving data tool. The final results say that the VK\u27s content has many opportunities to make them more useful and searchable, which means that it is possible to use this ‘property\u27 to create our own, more user-friendly way to search and get important data, in the first, for example, buying-selling information, from many kinds of data sources (official pages, users\u27 profiles etc.). That feature never been presented (and probably won\u27t) in other social networks like Facebook or Instagram. The material in this article will be used later while the author\u27s PhD thesis writing

Data-driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.Comment: 8 pages, 10 figures, ICDM 201

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Tracking fraudulent and low-quality display impressions

Display advertising is traded in a complex market with multiple sides and intermediaries, where advertisers are exposed to several forms of potentially fraudulent behavior. Intermediaries often claim to implement measures to detect fraud but provide limited information about those measures. Advertisers are required to trust that self-regulation efforts effectively filter out low-quality ad impressions. In this article, we propose an approach for tracking key display impression metrics by embedding a light JavaScript code in the ad to collect the necessary information to help detect fraudulent activities. We explain these metrics using the campaign cost per thousand (CPT) and the number of impressions per publisher. We test the approach through six display ad campaigns. Our results provide a counterargument against the industry claim that it is effectively filtering out display fraud and show the utility of our approach for advertisers.This work is partly supported by the European Union through SMOOTH (786741) and PIMCITY (871370); the European Social Fund through Ramón y Cajal (RYC-2015-17732); and the Spanish Ministry of Economy and Competitiveness through ECO2015-67763-R and PGC2018-096083-B-I00 projects

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

A Knowledge Management Approach to Identify Victims of Human Sex Trafficking

Social media and the interactive Web have enabled human traffickers to lure victims and then sell them faster and in greater safety than ever before. However, these same tools have also enabled investigators in their search for victims and criminals. We used system development action research methodology to create and apply a prototype designed to identify victims of human sex trafficking by analyzing online ads. The prototype used a knowledge management approach of generating actionable intelligence by applying a set of strong filters based on an ontology to identify potential victims. We used the prototype to analyze a data set generated from online ads. We used the results of this process to generate a revised prototype that included the use of machine learning and text mining enhancements. We used the revised prototype to identify potential victims in a second data set. The results of applying the prototypes suggest a viable approach to identifying victims of human sex trafficking in online ads

Matching Contextual Ads and Web Page Contents through Computational Advertising: Getting the Best Match

The technological transformation and automation of digital content delivery has revolutionized the media industry. What is more, the Internet is rapidly turning into an advertising channel. Just in the United States, Internet advertising revenues hit $7.3 billion for the first quarter of 2011, representing a 23 percent increase over the same period in 2010 (iab.net, 2011). Beneficiaries of this investment and growth are search engines such as Google, Yahoo, and MSN. Also, Malaysian advertising landscape is gradually shifting its traditional media forms to the emergent of Internet advertising but still at a budding stage. The latter shows much room for growth, as the industry fuels to content digitization on Web applications. In this project, the types of Internet advertising that is going to be discussed on are Contextual Ads and Sponsored Search Ads, but the major scope will be on Contextual Advertising. Given that, these types of advertising have the central challenge of finding the “best match” between a given context and a suitable advertisement, through principled way of computational methods. Hence, it is also referred as Computational advertising. Furthermore, there are four main players that exists in the Internet advertising ecosystem that are going to be discussed in this study, which are; Users, Advertisers, Ad Exchange and Publishers. Hence in order to find ways to counter the centre challenge, this research study will mainly address two objectives, which are to successfully make the best Contextual Ads selections that match to the Web Page contents through the concept of Computational advertising, and to ensure that there is a valuable connection between the Web pages and the Contextual Ads. Thus, the scope of the study will be mainly on discussing about the theory of Computational advertising itself, besides elaborating on Contextual Ads, matching Contextual Ads and Web pages and also, finding the most feasible way in creating the valuable connection between Contextual Ads and the Web pages. Moreover, at the end of every discussion in every subtopic, some insights on the Internet advertising in Malaysian context are discussed as per related issue. v Consequently, this study employed two main methods to address the research questions rose. Those methods include extensive research and analysis on previous literature works and journals, and also in depth surveys to collect related data and information in real-life situations. Every part of gathered data and findings will then be analyzed accordingly. All discussions, conclusion and future recommendations are presented as per sections. Hence in order to prove the working mechanism of matching Contextual Ads and Web pages by using Computational advertising approach, Web pages together with the ads matching system, will then be developed through FYP-II timeline, as the final product of the study