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

Measuring vertex centrality in co-occurrence graphs for online social tag recommendation

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of ECML PKDD (The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) Discovery Challenge 2009, Bled, Slovenia, September 7, 2009.We present a social tag recommendation model for collaborative bookmarking systems. This model receives as input a bookmark of a web page or scientific publication, and automatically suggests a set of social tags useful for annotating the bookmarked document. Analysing and processing the bookmark textual contents - document title, URL, abstract and descriptions - we extract a set of keywords, forming a query that is launched against an index, and retrieves a number of similar tagged bookmarks. Afterwards, we take the social tags of these bookmarks, and build their global co-occurrence sub-graph. The tags (vertices) of this reduced graph that have the highest vertex centrality constitute our recommendations, whThis research was supported by the European Commission under contracts FP6-027122-SALERO, FP6-033715-MIAUCE and FP6-045032 SEMEDIA. The expressed content is the view of the authors but not necessarily the view of SALERO, MIAUCE and SEMEDIA projects as a whol

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

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

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Benchmarking data mining approaches for traveler segmentation

The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers

Sistema de recomendación de programas universitarios para estudiantes de educación media basado en Deep Learning

High school students who are faced with the selection of academic programs decide based on program information available in search engines, websites, vocational counselling by advisors, or tests. However, these alternatives have limitations because they do not take into important historical and sociodemographic information, or in case of the advisors, they cannot guide all students. This work supports the decision-making of students through a Recommendation System that presents recommendations based on sociodemographic variables and historical academic data. Also, we propose and compare two methods:   a classic Collaborative Filtering model and a Deep Learning model.Los estudiantes que van a culminar la educación media y se enfrentan a la selección de programas académicos, usualmente usan buscadores web, información de programas y asesorías o pruebas vocacionales. Sin embargo, estas alternativas tienen limitaciones, porque no tienen en cuenta las características sociodemográficas del estudiante ni su desempeño académico o no pueden guiar adecuadamente a todos los estudiantes. Esta propuesta apoya la toma de decisiones de este grupo poblacional con un Sistema de Recomendación que produce recomendaciones basadas en variables sociodemográficas y datos académicos históricos de estudiantes de pregrado.  Además, se compara el desempeño de un modelo de Filtrado Colaborativo clásico y Deep Learning

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Integrating Ontologies and Fuzzy Logic to Represent User-Trustworthiness in Recommender Systems

Information Technology and Quantitative Management (ITQM 2015)Recommender systems can be used to assist users in the process of accessing to relevant information. In the literature we can find sundry approaches for generating personalized recommendations and all of them make use of different users’ and/or items’ features. Building accurate profiles plays an essential role in this context, so that the system's success depend to a large extent on the ability of the learned profiles to represent the user's preferences. An ontology works very well to characterize the users profiles. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modelling, this way in the recommendation generation process we do not take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback.Projects UJA2013/08/41TIN2013-40658-PTIC5299TIC-5991TIN2012-36951 co-financed by FEDER and TIC610