Ontology-Based Recommendation of Editorial Products

Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution

Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

THE USE OF RECOMMENDER SYSTEMS IN WEB APPLICATIONS – THE TROI CASE

Avoiding digital marketing, surveys, reviews and online users behavior approaches on digital age are the key elements for a powerful businesses to fail, there are some systems that should preceded some artificial intelligence techniques. In this direction, the use of data mining for recommending relevant items as a new state of the art technique is increasing user satisfaction as well as the business revenues. And other related information gathering approaches in order to our systems thing and acts like humans. To do so there is a Recommender System that will be elaborated in this thesis. How people interact, how to calculate accurately and identify what people like or dislike based on their online previous behaviors. The thesis includes also the methodologies recommender system uses, how math equations helps Recommender Systems to calculate user’s behavior and similarities. The filters are important on Recommender System, explaining if similar users like the same product or item, which is the probability of neighbor user to like also. Here comes collaborative filters, neighborhood filters, hybrid recommender system with the use of various algorithms the Recommender Systems has the ability to predict whether a particular user would prefer an item or not, based on the user’s profile and their activities. The use of Recommender Systems are beneficial to both service providers and users. Thesis cover also the strength and weaknesses of Recommender Systems and how involving Ontology can improve it. Ontology-based methods can be used to reduce problems that content-based recommender systems are known to suffer from. Based on Kosovar’s GDP and youngsters job perspectives are desirable for improvements, the demand is greater than the offer. I thought of building an intelligence system that will be making easier for Kosovars to find the appropriate job that suits their profile, skills, knowledge, character and locations. And that system is called TROI Search engine that indexes and merge all local operating job seeking websites in one platform with intelligence features. Thesis will present the design, implementation, testing and evaluation of a TROI search engine. Testing is done by getting user experiments while using running environment of TROI search engine. Results show that the functionality of the recommender system is satisfactory and helpful

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

“WARES”, a Web Analytics Recommender System

Il est difficile d'imaginer des entreprises modernes sans analyse, c'est une tendance dans les entreprises modernes, même les petites entreprises et les entrepreneurs individuels commencent à utiliser des outils d'analyse d'une manière ou d'une autre pour leur entreprise. Pas étonnant qu'il existe un grand nombre d'outils différents pour les différents domaines, ils varient dans le but de simples statistiques d'amis et de visites pour votre page Facebook à grands et sophistiqués dans le cas des systèmes conçus pour les grandes entreprises, ils pourraient être shareware ou payés. Parfois, vous devez passer une formation spéciale, être un spécialiste certifiés, ou même avoir un diplôme afin d'être en mesure d'utiliser l'outil d'analyse. D'autres outils offrent une interface d’utilisateur simple, avec des tableaux de bord, pour satisfaire leur compréhension d’information pour tous ceux qui les ont vus pour la première fois. Ce travail sera consacré aux outils d'analyse Web. Quoi qu'il en soit pour tous ceux qui pensent à utiliser l'analyse pour ses propres besoins se pose une question: "quel outil doit je utiliser, qui convient à mes besoins, et comment payer moins et obtenir un gain maximum". Dans ce travail je vais essayer de donner une réponse sur cette question en proposant le système de recommandation pour les outils analytiques web –WARES, qui aideront l'utilisateur avec cette tâche "simple". Le système WARES utilise l'approche hybride, mais surtout, utilise des techniques basées sur le contenu pour faire des suggestions. Le système utilise certains ratings initiaux faites par utilisateur, comme entrée, pour résoudre le problème du “démarrage à froid”, offrant la meilleure solution possible en fonction des besoins des utilisateurs. Le besoin de consultations coûteuses avec des experts ou de passer beaucoup d'heures sur Internet, en essayant de trouver le bon outil. Le système lui–même devrait effectuer une recherche en ligne en utilisant certaines données préalablement mises en cache dans la base de données hors ligne, représentée comme une ontologie d'outils analytiques web existants extraits lors de la recherche en ligne précédente.It is hard to imagine modern business without analytics; it is a trend in modern business, even small companies and individual entrepreneurs start using analytics tools, in one way or another, for their business. Not surprising that there exist many different tools for different domains, they vary in purpose from simple friends and visits statistic for your Facebook page, to big and sophisticated systems designed for the big corporations, they could be free or paid. Sometimes you need to pass special training, be a certified specialist, or even have a degree to be able to use analytics tool, other tools offers simple user interface with dashboards for easy understanding and availability for everyone who saw them for the first time. Anyway, for everyone who is thinking about using analytics for his/her own needs stands a question: “what tool should I use, which one suits my needs and how to pay less and get maximum gain”. In this work, I will try to give an answer to this question by proposing a recommender tool, which will help the user with this “simple task”. This paper is devoted to the creation of WARES, as reduction from Web Analytics REcommender System. Proposed recommender system uses hybrid approach, but mostly, utilize content–based techniques for making suggestions, while using some user’s ratings as an input for “cold start” search. System produces recommendations depending on user’s needs, also allowing quick adjustments in selection without need of expensive consultations with experts or spending lots of hours for Internet search, trying to find out the right tool. The system itself should perform as an online search using some pre–cached data in offline database, represented as an ontology of existing web analytics tools, extracted during the previous online search