Building an Multi-Agent Whisky Recommender System

MAS (Multi-Agent Systems), classifiers and other AI (Artificial Intelligence) techniques are increasingly becoming more common. The capability to handle complex and advanced problems by MAS was explored in this thesis. An MAS duty-free shopping recommender system was built for this purpose. The MAS system was part of a larger system built by the AmbieSense project. In addition, the AmbieSense project had built a prototype that was tested at Oslo Airport (OSL) Gardermoen. As a test case, the duty-free shopping system was set to classify and recommend whiskies. The system incorporated several AI techniques such as agents, ontology, knowledge base and classifiers. The MAS was built using the JADE-LEAP framework, and Protégé was used for constructing the knowledge base. Various tests were performed for testing the system. Firstly, the agent communication was monitored using a Sniffer Agent. Secondly, the system’s ability to run on mobile devices was tested on a PDA and various mobile phones. Thirdly, the MAS abilities compared to a ‘normal’ computer program were tested by replacing agents at run-time, using several JADE platforms, and by the experience gathered during development and the use of the developed system. Lastly, the recommendation was cross-validated against Dr. Wishart’s whisky classification. Weka was employed as a tool for testing different features and classifiers. Different classification algorithms are explained such as NNR (Nearest-Neighbour Rule), Bayesian, CBR (Case-Based Reasoning), cluster analysis and self-organizing feature maps. Two classification algorithms were tested; NNR and Bayesian. Features were tested using feature evaluation algorithms; information gain and ReliefF. The accuracy of the classification was tested using 10 fold cross-validation. The testing showed that it is possible to make an MAS handling complex and advanced problems. It has also been shown that an MAS have some benefits in the areas of reliability, extensibility, computational efficiency and maintainability when compared to a ‘normal’ program. The error rate produced by the classifier was 56%; a figure which is too high for a recommendation system. Improvements could probably be achieved by finding better features or by selecting a different classifier. The system developed does not necessarily have to be used for duty-free shopping but could also be used for any shopping items

Social and Content-based Information Filtering for a Web Graphics Recommender System

Filtering-by-example is an information filtering method that enables a user to refine filtering results by indicating examples of what he or she wants. However, the existing social and content-based approaches are difficult to apply to image data. To realize a filtering-by-example system for image data, we propose a new approach to combine social and content-based filtering techniques. A content-based sub-system provides two types of clusters, equivalent items and virtual users, to overcome a disadvantage of social filtering: a shortage of ratings. Since items similar in visual properties are not always similar in users' tastes, a social sub-system controls the content-based sub-system with an evaluation function that estimates the validity of content-based clusters according to user ratings. Based on this approach, we have developed an image database, Web Graphics Navigator, that recommends graphics for web pages according to the users' tastes. The database has been open to the public..

Modelado de sistemas multimedia para personalización y recomendación híbrida a partir del consumo audiovisual de los usuarios

This doctoral thesis focuses on the modeling of multimedia systems to create personalized recommendation services based on the analysis of users’ audiovisual consumption. Research is focused on the characterization of both users’ audiovisual consumption and content, specifically images and video. This double characterization converges into a hybrid recommendation algorithm, adapted to different application scenarios covering different specificities and constraints. Hybrid recommendation systems use both content and user information as input data, applying the knowledge from the analysis of these data as the initial step to feed the algorithms in order to generate personalized recommendations. Regarding the user information, this doctoral thesis focuses on the analysis of audiovisual consumption to infer implicitly acquired preferences. The inference process is based on a new probabilistic model proposed in the text. This model takes into account qualitative and quantitative consumption factors on the one hand, and external factors such as zapping factor or company factor on the other. As for content information, this research focuses on the modeling of descriptors and aesthetic characteristics, which influence the user and are thus useful for the recommendation system. Similarly, the automatic extraction of these descriptors from the audiovisual piece without excessive computational cost has been considered a priority, in order to ensure applicability to different real scenarios. Finally, a new content-based recommendation algorithm has been created from the previously acquired information, i.e. user preferences and content descriptors. This algorithm has been hybridized with a collaborative filtering algorithm obtained from the current state of the art, so as to compare the efficiency of this hybrid recommender with the individual techniques of recommendation (different hybridization techniques of the state of the art have been studied for suitability). The content-based recommendation focuses on the influence of the aesthetic characteristics on the users. The heterogeneity of the possible users of these kinds of systems calls for the use of different criteria and attributes to create effective recommendations. Therefore, the proposed algorithm is adaptable to different perceptions producing a dynamic representation of preferences to obtain personalized recommendations for each user of the system. The hypotheses of this doctoral thesis have been validated by conducting a set of tests with real users, or by querying a database containing user preferences - available to the scientific community. This thesis is structured based on the different research and validation methodologies of the techniques involved. In the three central chapters the state of the art is studied and the developed algorithms and models are validated via self-designed tests. It should be noted that some of these tests are incremental and confirm the validation of previously discussed techniques. Resumen Esta tesis doctoral se centra en el modelado de sistemas multimedia para la creación de servicios personalizados de recomendación a partir del análisis de la actividad de consumo audiovisual de los usuarios. La investigación se focaliza en la caracterización tanto del consumo audiovisual del usuario como de la naturaleza de los contenidos, concretamente imágenes y vídeos. Esta doble caracterización de usuarios y contenidos confluye en un algoritmo de recomendación híbrido que se adapta a distintos escenarios de aplicación, cada uno de ellos con distintas peculiaridades y restricciones. Todo sistema de recomendación híbrido toma como datos de partida tanto información del usuario como del contenido, y utiliza este conocimiento como entrada para algoritmos que permiten generar recomendaciones personalizadas. Por la parte de la información del usuario, la tesis se centra en el análisis del consumo audiovisual para inferir preferencias que, por lo tanto, se adquieren de manera implícita. Para ello, se ha propuesto un nuevo modelo probabilístico que tiene en cuenta factores de consumo tanto cuantitativos como cualitativos, así como otros factores de contorno, como el factor de zapping o el factor de compañía, que condicionan la incertidumbre de la inferencia. En cuanto a la información del contenido, la investigación se ha centrado en la definición de descriptores de carácter estético y morfológico que resultan influyentes en el usuario y que, por lo tanto, son útiles para la recomendación. Del mismo modo, se ha considerado una prioridad que estos descriptores se puedan extraer automáticamente de un contenido sin exigir grandes requisitos computacionales y, de tal forma que se garantice la posibilidad de aplicación a escenarios reales de diverso tipo. Por último, explotando la información de preferencias del usuario y de descripción de los contenidos ya obtenida, se ha creado un nuevo algoritmo de recomendación basado en contenido. Este algoritmo se cruza con un algoritmo de filtrado colaborativo de referencia en el estado del arte, de tal manera que se compara la eficiencia de este recomendador híbrido (donde se ha investigado la idoneidad de las diferentes técnicas de hibridación del estado del arte) con cada una de las técnicas individuales de recomendación. El algoritmo de recomendación basado en contenido que se ha creado se centra en las posibilidades de la influencia de factores estéticos en los usuarios, teniendo en cuenta que la heterogeneidad del conjunto de usuarios provoca que los criterios y atributos que condicionan las preferencias de cada individuo sean diferentes. Por lo tanto, el algoritmo se adapta a las diferentes percepciones y articula una metodología dinámica de representación de las preferencias que permite obtener recomendaciones personalizadas, únicas para cada usuario del sistema. Todas las hipótesis de la tesis han sido debidamente validadas mediante la realización de pruebas con usuarios reales o con bases de datos de preferencias de usuarios que están a disposición de la comunidad científica. La diferente metodología de investigación y validación de cada una de las técnicas abordadas condiciona la estructura de la tesis, de tal manera que los tres capítulos centrales se estructuran sobre su propio estudio del estado del arte y los algoritmos y modelos desarrollados se validan mediante pruebas autónomas, sin impedir que, en algún caso, las pruebas sean incrementales y ratifiquen la validación de técnicas expuestas anteriormente