Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

Dynamic user profiles for web personalisation

Web personalisation systems are used to enhance the user experience by providing tailor-made services based on the user’s interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users’ changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalisation system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilises our dynamic user profile to provide a personalised search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours

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

COMMUNITY DETECTION AND INFLUENCE MAXIMIZATION IN ONLINE SOCIAL NETWORKS

The detecting and clustering of data and users into communities on the social web are important and complex issues in order to develop smart marketing models in changing and evolving social ecosystems. These marketing models are created by individual decision to purchase a product and are influenced by friends and acquaintances. This leads to novel marketing models, which view users as members of online social network communities, rather than the traditional view of marketing to individuals. This thesis starts by examining models that detect communities in online social networks. Then an enhanced approach to detect community which clusters similar nodes together is suggested. Social relationships play an important role in determining user behavior. For example, a user might purchase a product that his/her friend recently bought. Such a phenomenon is called social influence and is used to study how far the action of one user can affect the behaviors of others. Then an original metric used to compute the influential power of social network users based on logs of common actions in order to infer a probabilistic influence propagation model. Finally, a combined community detection algorithm and suggested influence propagation approach reveals a new influence maximization model by identifying and using the most influential users within their communities. In doing so, we employed a fuzzy logic based technique to determine the key users who drive this influence in their communities and diffuse a certain behavior. This original approach contrasts with previous influence propagation models, which did not use similarity opportunities among members of communities to maximize influence propagation. The performance results show that the model activates a higher number of overall nodes in contemporary social networks, starting from a smaller set of key users, as compared to existing landmark approaches which influence fewer nodes, yet employ a larger set of key users

Semantic recommender systems Provision of personalised information about tourist activities.

Aquesta tesi estudia com millorar els sistemes de recomanació utilitzant informació semàntica sobre un determinat domini (en el cas d’aquest treball, Turisme). Les ontologies defineixen un conjunt de conceptes relacionats amb un determinat domini, així com les relacions entre ells. Aquestes estructures de coneixement poden ser utilitzades no només per representar d'una manera més precisa i refinada els objectes del domini i les preferències dels usuaris, sinó també per millorar els procediments de comparació entre els objectes i usuaris (i també entre els mateixos usuaris) amb l'ajuda de mesures de similitud semàntica. Les millores al nivell de la representació del coneixement i al nivell de raonament condueixen a recomanacions més precises i a una millora del rendiment dels sistemes de recomanació, generant nous sistemes de recomanació semàntics intel•ligents. Les dues tècniques bàsiques de recomanació, basades en contingut i en filtratge col•laboratiu, es beneficien de la introducció de coneixement explícit del domini. En aquesta tesi també hem dissenyat i desenvolupat un sistema de recomanació que aplica els mètodes que hem proposat. Aquest recomanador està dissenyat per proporcionar recomanacions personalitzades sobre activitats turístiques a la regió de Tarragona. Les activitats estan degudament classificades i etiquetades d'acord amb una ontologia específica, que guia el procés de raonament. El recomanador té en compte molts tipus diferents de dades: informació demogràfica, les motivacions de viatge, les accions de l'usuari en el sistema, les qualificacions proporcionades per l'usuari, les opinions dels usuaris amb característiques demogràfiques similars o gustos similars, etc. Un procés de diversificació que calcula similituds entre objectes s'aplica per augmentar la varietat de les recomanacions i per tant augmentar la satisfacció de l'usuari. Aquest sistema pot tenir un impacte positiu a la regió en millorar l'experiència dels seus visitants.Esta tesis estudia cómo mejorar los sistemas de recomendación utilizando información semántica sobre un determinado dominio, en el caso de este trabajo el Turismo. Las ontologías definen un conjunto de conceptos relacionados con un determinado dominio, así como las relaciones entre ellos. East estructuras de conocimiento pueden ser utilizadas no sólo para representar de una manera más precisa y refinada los objetos del dominio y las preferencias de los usuarios, sino también para aplicar mejor los procedimientos de comparación entre los objetos y usuarios (y también entre los propios usuarios) con la ayuda de medidas de similitud semántica. Las mejoras al nivel de la representación del conocimiento y al nivel de razonamiento conducen a recomendaciones más precisas y a una mejora del rendimiento de los sistemas de recomendación, generando nuevos sistemas de recomendación semánticos inteligentes. Las dos técnicas de recomendación básicas, basadas en contenido y en filtrado colaborativo, se benefician de la introducción de conocimiento explícito del dominio. En esta tesis también hemos diseñado y desarrollado un sistema de recomendación que aplica los métodos que hemos propuesto. Este recomendador está diseñado para proporcionar recomendaciones personalizadas sobre las actividades turísticas en la región de Tarragona. Las actividades están debidamente clasificadas y etiquetadas de acuerdo con una ontología específica, que guía el proceso de razonamiento. El recomendador tiene en cuenta diferentes tipos de datos: información demográfica, las motivaciones de viaje, las acciones del usuario en el sistema, las calificaciones proporcionadas por el usuario, las opiniones de los usuarios con características demográficas similares o gustos similares, etc. Un proceso de diversificación que calcula similitudes entre objetos se aplica para generar variedad en las recomendaciones y por tanto aumentar la satisfacción del usuario. Este sistema puede tener un impacto positivo en la región al mejorar la experiencia de sus visitantes.This dissertation studies how new improvements can be made on recommender systems by using ontological information about a certain domain (in the case of this work, Tourism). Ontologies define a set of concepts related to a certain domain as well as the relationships among them. These knowledge structures may be used not only to represent in a more precise and refined way the domain objects and the user preferences, but also to apply better matching procedures between objects and users (or between users themselves) with the help of semantic similarity measures. The improvements at the knowledge representation level and at the reasoning level lead to more accurate recommendations and to an improvement of the performance of recommender systems, paving the way towards a new generation of smart semantic recommender systems. Both content-based recommendation techniques and collaborative filtering ones certainly benefit from the introduction of explicit domain knowledge. In this thesis we have also designed and developed a recommender system that applies the methods we have proposed. This recommender is designed to provide personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labelled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. A diversification process that computes similarities between objects is applied to produce diverse recommendations and hence increase user satisfaction. This system can have a beneficial impact on the region by improving the experience of its visitors

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention

Computing point-of-view : modeling and simulating judgments of taste

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 153-163).People have rich points-of-view that afford them the ability to judge the aesthetics of people, things, and everyday happenstance; yet viewpoint has an ineffable quality that is hard to articulate in words, let alone capture in computer models. Inspired by cultural theories of taste and identity, this thesis explores end-to-end computational modeling of people's tastes-from model acquisition, to generalization, to application- under various realms. Five aesthetical realms are considered-cultural taste, attitudes, ways of perceiving, taste for food, and sense-of-humor. A person's model is acquired by reading her personal texts, such as a weblog diary, a social network profile, or emails. To generalize a person model, methods such as spreading activation, analogy, and imprimer supplementation are applied to semantic resources and search spaces mined from cultural corpora. Once a generalized model is achieved, a person's tastes are brought to life through perspective-based applications, which afford the exploration of someone else's perspective through interactivity and play. The thesis describes model acquisition systems implemented for each of the five aesthetical realms.(cont.) The techniques of 'reading for affective themes' (RATE), and 'culture mining' are described, along with their enabling technologies, which are commonsense reasoning and textual affect analysis. Finally, six perspective-based applications were implemented to illuminate a range of real-world beneficiaries to person modeling-virtual mentoring, self-reflection, and deep customization.by Xinyu Hugo Liu.Ph.D

Design and development of a REST-based Web service platform for applications integration

Web services have attracted attention as a possible solution to share knowledge and application logic among different heterogeneous agents. A classic approach to this subject is using SOAP, a W3C protocol aimed to exchange structured information. The Web Services Interoperability organization (WS-I), defines a set of extensions, commonly called WS-*, which further enhance this knowledge exchange defining mechanisms and functionalities such as security, addressability or service composition. This thesis explores a relatively new alternative approach to the SOAP/WS-I stack: REST-based Web services. The acronym REST stands for Representational state transfer; this basically means that each unique URL is a representation of some object. You can get the contents of that object using an HTTP GET; you then might use a POST, PUT or DELETE to modify the object (in practice most of the services use a POST for this). All of Yahoo’s Web services use REST, including Flickr; del.icio.us API uses it; pubsub [http://www.pubsub.com/], Bloglines [http://www.bloglines.com/], Technorati [http://technorati.com/] and both, eBay and Amazon, have Web services for both REST and SOAP. Google seems to be consistent in implementing their Web services to use SOAP, with the exception of Blogger, which uses XML-RPC. The companies and organization that are using REST APIs have not been around for very long, and their APIs came out in the last seven years mostly. So REST is a new way to create and integrate Web services, whose main advantages are: being lightweight (not a lot of extra xml mark-up), human readable results, easy to build services (no toolkits required). Although REST is still generating discussion about possible implementations, and different proposals have been put forward, it provides enough mechanisms to allow knowledge-representations sharing among heterogeneous intelligent services. In this thesis, a novel way to integrate intelligent Web-services is designed and developed, and the resulting system is deployed in the domain of recommendation. Through a mashup, how different services are integrated and how a simple recommendation system consumes data coming from them to provide relevant information to users is presented. Part of this work has been carried out within the context of the Laboranova European project [http://www.laboranova.com/], and has been deployed to integrate a set of applications to create a virtual space to support innovation processes

Privacy safeguards and online anonymity

In a world that is increasingly more connected, digital citizens, actively or passively accept to transmit information, part of which are “personal data”. This information is often collected and elaborated by third parties to infer further knowledge about users. The act of gathering the data is commonly called “tracking” and can be performed through several means. The act of analysing and processing those data and relate them to the individual is called “profiling”. The aim of this JRC Technical report is to be an instrument of support for the Digital Citizens to help them to protect and to manage their privacy during online activities. After a brief introduction in Chapter 1, the following chapter is dedicated to the description of two legitimate use-cases to track and profile users on-line, namely target advertising and personalisation of the user experience. Chapter 3 and 4 identify and analyse the set of techniques currently used by online digital providers to track citizens and profile them based on their online behaviour. Chapter 5 deals with some of the available tools cited in chapter 6 that could be helpful to protect the privacy while browsing online. Chapter 6 aims to raise awareness among users and provide some guidelines to address specific issues related to privacy through a multidisciplinary approach. The report concludes highlighting the importance of raising awareness among digital users and empower them through education, technical and legal tools, including the General Data Protection Regulation (GDPR) to overcome possible privacy issues.JRC.E.3-Cyber and Digital Citizens' Securit