Methods and applications for ontology-based recommender systems

Recommender systems are a specific type of information filtering systems used to identify a set of objects that are relevant to a user. Instead of a user actively searching for information, recommender systems provide advice to users about objects they might wish to examine. Content-based recommender systems deal with problems related to analyzing the content, making heterogeneous content interoperable, and retrieving relevant content for the user. This thesis explores ontology-based methods to reduce these problems and to evaluate the applicability of the methods in recommender systems. First, the content analysis is improved by developing an automatic annotation method that produces structured ontology-based annotations from text. Second, an event-based method is developed to enable interoperability of heterogeneous content representations. Third, methods for semantic content retrieval are developed to determine relevant objects for the user. The methods are implemented as part of recommender systems in two cultural heritage information systems: CULTURESAMPO and SMARTMUSEUM. The performance of the methods were evaluated through user studies. The results can be divided into five parts. First, the results show improvement in automatic content analysis compared to state of the art methods and achieve performance close to human annotators. Second, the results show that the event-based method developed is suitable for bridging heterogeneous content representations. Third, the retrieval methods show accurate performance compared to user opinions. Fourth, semantic distance measures are compared to study the best query expansion strategy. Finally, practical solutions are developed to enable user profiling and result clustering. The results show that ontology-based methods enable interoperability of heterogeneous knowledge representations and result in accurate recommendations. The deployment of the methods to practical recommender systems show applicability of the results in real life settings

Enhancing the ELECTRE decision support method with semantic data

Prendre una decisió quan les opcions es defineixen mitjançant un conjunt divers de criteris no és fàcil. Aqueta tesi es centra en ampliar la metodologia ELECTRE, que és el mètode del tipus "outranking" més utilitzat. En aquesta tesi ens centrem en problemes de decisió que involucren informació no numèrica, tal com els criteris semàntics multivaluats, que poden prendre com a valors els conceptes d'una ontologia de domini determinada. Primer proposo una nova manera de manipular els criteris semàntics per evitar l'agregació de les puntuacions numèriques abans del procediment de classificació. Aquest mètode, anomenat ELECTRE-SEM, segueix els mateixos principis que el clàssic ELECTRE però, en aquest cas, els índexs de concordança i discordança es defineixen en termes de la comparació per parelles de les puntuacions que indiquen l'interès de l'usuari sobre diferents conceptes de l'ontologia. En segon lloc, proposo crear un perfil d'usuari semàntic mitjançant el emmagatzemant de puntuacions de preferències a l'ontologia. Es vincula una puntuació d'interès numèrica als conceptes més específics, això permet distingir millor les preferències de l'usuari, i també s'incorpora un procediment d'agregació per inferir les preferències de l'usuari considerant les relacions taxonòmiques entre conceptes. La metodologia proposada s'ha aplicat en dos casos d’estudi: l'avaluació de plantes de generació d'energia i la recomanació d'activitats turístiques a Tarragona.Tomar una decisión cuando las opciones se definen sobre un conjunto diverso de criterios no es fácil. Esta tesis se centra en ampliar la metodología ELECTRE, que es el método del tipo "outranking" más utilizado. En esta tesis nos centramos en problemas de decisión que involucren información no numérica, tal como los criterios semánticos multi-valuados, que pueden tomar como valores los conceptos de una ontología de dominio determinada. Primero propongo una nueva forma de manejar los criterios semánticos para evitar la agregación de puntuaciones numéricas antes del procedimiento de clasificación. Este método, llamado ELECTRE-SEM, sigue los mismos principios que el clásico ELECTRE, pero en este caso los índices de concordancia y discordancia se definen en términos de la comparación por pares de unas puntuaciones que indican el interés del usuario sobre distintos conceptos de la ontología. En segundo lugar, propongo crear un perfil de usuario semántico mediante el almacenamiento de puntuaciones de preferencias en la ontología. Se asocian puntuaciones numéricas a los conceptos más específicos, lo cual permite distinguir mejor las preferencias del usuario, y se incorpora un proceso de agregación para inferir las preferencias del usuario mediante las relaciones taxonómicas entre conceptos. La metodología propuesta ha sido aplicada en dos casos de estudio: la evaluación de las plantas de generación de energía y la recomendación de actividades turísticas en Tarragona.Reach a decision when options are defined on a set of diverse criteria is not easy. This thesis is focused on improving the methodology ELECTRE, which is the most used outranking-based method. In this dissertation, we focus on decision problems involving non-numerical information, such as multi-valued semantic criteria, which may take as values the concepts of a given domain ontology. First, I propose a new way of handling semantic criteria to avoid the aggregation of the numerical scores before the ranking procedure. This method, called ELECTRE-SEM, follows the same principles than the classic ELECTRE but in this case the concordance and discordance indices are defined in terms of the pairwise comparison of the interest scores. Second, I also propose to create a semantic user profile by storing preference scores into the ontology. The numerical interest score attached to the most specific concepts permits to distinguish better the preferences of the user, improving the quality of the decision by the incorporation of an aggregation methodology to infer the user's preferences by considering taxonomic relations between concepts. The proposed methodology has been applied in two case studies: the assessment of power generation plants and the recommendation of touristic activities in Tarragona

Inferring semantic relations by user feedback

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems

Implementing sharing platform based on ontology using a sequential recommender system

While recommender systems have shown success in many fields, accurate recommendations in industrial settings remain challenging. In maintenance, existing techniques often struggle with the “cold start” problem and fail to consider differences in the target population's characteristics. To address this, additional user information can be incorporated into the recommendation process. This paper proposes a recommender system for recommending repair actions to technicians based on an ontology (knowledge base) and a sequential model. The approach utilizes two ontologies, one representing failure knowledge and the other representing asset attributes. The proposed method involves two steps: i) calculating score similarity based on ontology domain knowledge to make predictions for targeted failures and ii) generating Top-N repair actions through collaborative filtering recommendations for targeted failures. An additional module was implemented to evaluate the recommender system, and results showed improved performance

EduCOR: An Educational and Career-Oriented Recommendation Ontology

With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations. Resource: https://tibonto.github.io/educor/

Ontology-style web usage model for semantic Web applications

Current semantic recommender systems aim to exploit the website ontologies to produce valuable web recommendations. However, Web usage knowledge for recommendation is presented separately and differently from the domain ontology, this leads to the complexity of using inconsistent knowledge resources. This paper aims to solve this problem by proposing a novel ontology-style model of Web usage to represent the non-taxonomic visiting relationship among the visited pages. The output of this model is an ontology-style document which enables the discovered web usage knowledge to be sharable and machine-understandable in semantic Web applications, such as recommender systems. A case study is presented to show how this model is used in conjunction of the web usage mining and web recommendation. Two real-world datasets are used in the case study. © 2010 IEEE

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision