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

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

Exploring enterprises competition: From a perspective of massive recruitment texts mining

Extant research has made limited efforts to conduct competitive intelligence analysis based on recruitment texts. To fill the gap, this study proposes a method for deriving and analyzing competitive relationships, identifying competition paths, and calculating asymmetric competitiveness degrees, from the recruitment texts on e-recruiting websites. Specifically, this study developed a competitive evaluation index system for companies’ skill needs and resource base based on 53,171 job descriptions and 42,641 company profiles published by companies across 8 industries (including 35 industry segments) using automated text processing methods. Furthermore, in order to identify competitive paths and calculate the degree of asymmetric competitiveness, this study proposes a modified bipartite graph approach (i.e., MBGA) for competitive intelligence analysis of recruitment texts based on the competition evaluation index system. Experiments on a real-world dataset of the representative companies clearly validated the effectiveness of the method. Compared to the five state-of-the-art methods, MBGA performs better in disclosing the overall competition and is more accurate in terms of the error rating ratio (i.e., ERR) of the competition

The Present and Future of Internet Search

Search engines were crucial in the development of the World Wide Web. Web-based information retrieval progressed from simple word matching to sophisticated algorithms for maximizing the relevance of search results. Statistical and graph-based approaches for indexing and ranking pages, natural language processing techniques for improving query results, and intelligent agents for personalizing the search process all show great promise for enhanced performance. The evolution in search technology was accompanied by growing economic pressures on search engine companies. Unable to sustain long-term viability from advertising revenues, many of the original search engines diversified into portals that farm out their search and directory operations. Vertical portals that serve focused user communities also outsource their search services, and even directory providers began to integrate search engine technologies from outside vendors. This article brings order to the chaos resulting from the variety of search tools being offered under various marketing guises. While growing reliance on a small set of search providers is leading to less diversity among search services, users can expect individualized searching experiences that factor in personal information. The convergence of technology and business models also results in more narrowly defined search spaces, which will lessen the quantity of search results while improving their quality

Decision support system for search engine advertising campaign management by determining negative keywords

Search engine advertisers need to determine the best keyword set for their campaigns. Every company has particular constraints and expectations from the Search Engine Advertising (SEA). In this research we worked on a Decision Support System (DSS) that can be used in SEA campaign management. The DSS determines the negative keywords (which should be eliminated from the keyword set in order to improve the performance) based on the data obtained from the earlier campaigns. Current metrics used to determine the negative keywords are not sufficient/adequate, since they don’t incorporate other important aspects such as bounce rate, quality score etc. which are often used by the advertisers in order to evaluate the traffic but rely mostly to conversion rate. In our research first we analyze the keywords at unigram level (similar to some of the existing approaches available in the literature) in order to identify the set of unigrams which are negatively and/or positively effecting the campaign by using various machine learning techniques (either as is or used the core concepts associated with them) such as Naïve Bayes, Decision Trees, Logistic Regression. We further extended these algorithms by incorporating ideas borrowed from Greedy Randomized Adaptive Search (GRASP). We also introduced novel metrics which incorporate more aspects used in real life SEA campaigns by the advertisers as part of this process. The performance of our approach is evaluated with an experimental analysis conducted on real life data obtained from a major FMCG producer

Personalized Web Page Recommendation Using Ontology

In this network era, Web Page Recommendation and web page Recommendation systems can take advantage of semantic network reasoning-capabilities to overcome common limitations of current systems and improve the recommendations’ quality. This paper presents a personalized-web-recommendation system, a system that makes use of representations of items and user-profiles based on ontology in order to provide semantic applications with personalized services. The recommender uses domain ontology to enhance the personalization: on the other hand, user’s interests are modeled in a more effective and accurate way by applying a domain-based inference method; on the other hand, the stemmer algorithm used by our content-based filtering approach, which provides a measure of the affinity between an item and a user, is enhanced by applying a semantic similarity method. Web Usage Mining plays an important role in web page recommender systems and web personalization system. In this paper, we propose an effective personalized web recommendation system based on ontology and Web Usage Mining. The proposed approach integrates semantic knowledge into Web Usage Mining and personalization processes. DOI: 10.17762/ijritcc2321-8169.15071

Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research

Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the incomplete information of historical advertising performance indices and the high uncertainty in SSA environments. First, we construct a data distribution estimation model and apply a Markov Chain Monte Carlo method to make inference about unobserved indices (i.e., impression and click-through rate) over three keyword matching types (i.e., broad, phrase and exact). Second, we formulate a stochastic keyword targeting model (BB-KSM) combining operations of keyword selection and keyword matching to maximize the expected profit under the chance constraint of the budget, and develop a branch-and-bound algorithm incorporating a stochastic simulation process for our keyword targeting model. Finally, based on a realworld dataset collected from field reports and logs of past SSA campaigns, computational experiments are conducted to evaluate the performance of our keyword targeting strategy. Experimental results show that, (a) BB-KSM outperforms seven baselines in terms of profit; (b) BB-KSM shows its superiority as the budget increases, especially in situations with more keywords and keyword combinations; (c) the proposed data distribution estimation approach can effectively address the problem of incomplete performance indices over the three matching types and in turn significantly promotes the performance of keyword targeting decisions. This research makes important contributions to the SSA literature and the results offer critical insights into keyword management for SSA advertisers.Comment: 38 pages, 4 figures, 5 table

Google Search and the Law on Dominance in the EU:An Assessment of the Compatibility of Current Methodology with Multi-Sided Platforms in Online Search

Business platforms that utilise, or are based upon, internet technology are omnipresent in consumers daily lives. Since the dawn of the World Wide Web, the amount of web content has increased greatly. Simultaneously, business interests have sparked, meeting the arisen demand for particular online services. As a consequence, economists have defined a novel market in these sectors, namely that of multi-sided platform markets. To an important extent, these markets experience network effects, which can strengthen a platform operator’s position in relation to competitors. In turn, competition authorities have witnessed various dominant undertakings emerging. The focus of this article is on one particular internet sector, to wit, that of World Wide Web Search, and on one firm in particular, Google Incorporated. It critically analyses how the Google Search algorithms are shaped from a technological perspective, how these are or can be categorised in accordance with the economic theory of multi-sided platform markets, and how these perform under current dominance law analysis in the European Union, more specifically Art. 102 TFEU. To that end, it will also take into account the recent Google Commitments procedure by the European Commission