Sumea suositin elokuville

Suositusmenetelmien tarkoituksena on auttaa käyttäjää löytämään häntä kiinnostavia asioita ja välttämään asioita, joista hän ei pitäisi. Suositusmenetelmät antavat suosituk- set yleensä terävinä lukuina. Tässä työssä kehitetään suositusmenetelmä, joka antaa suo- situkset arvosanojen sumeina jäsenyysasteina. Menetelmän antamat suositukset voidaan myös perustella käyttäjälle. Menetelmä kuuluu pääosin yhteisösuodatusmenetelmiin, jois- sa suositukset tehdään käyttäjien antamien arvosanojen perusteella, mutta myös tietoa elokuvien tyylilajeista hyödynnetään suositustarkkuuden parantamiseksi. Sumeiden suo- situsten suositeltavuusjärjestyksen laskemiseen esitetään myös menetelmä. Käyttäjien elokuville antamat arvosanat voidaan käsittää sumeana datana. Käyttäjä voi kuvata arvosanaa esimerkiksi ilmaisulla ”noin 4”. Tästä syystä on loogista esittää suo- situksetkin sumeina lukuina. Tällöin käyttäjälle voidaan antaa tietoa suosituksen tark- kuudesta ja mahdollisista ristiriidoista. Epävarmojen suositusten tapauksessa käyttäjä voi painottaa enemmän muita tietolähteitä. Kokeiden perusteella kehitetty menetelmä antaa joissa tapauksissa selvästi vertailtavia menetelmiä parempia suosituksia, kun taas toisissa tapauksissa suositukset ovat selvästi heikompia.

LORESA : un système de recommandation d'objets d'apprentissage basé sur les annotations sémantiques

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Mures : Un système de recommandation de musique

Pendant la dernière décennie nous avons vu une transformation incroyable du monde de la musique qui est passé des cassettes et disques compacts à la musique numérique en ligne. Avec l'explosion de la musique numérique, nous avons besoin de systèmes de recommandation de musique pour choisir les chansons susceptibles d’être appréciés à partir de ces énormes bases de données en ligne ou personnelles. Actuellement, la plupart des systèmes de recommandation de musique utilisent l’algorithme de filtrage collaboratif ou celui du filtrage à base de contenu. Dans ce mémoire, nous proposons un algorithme hybride et original qui combine le filtrage collaboratif avec le filtrage basé sur étiquetage, amélioré par la technique de filtrage basée sur le contexte d’utilisation afin de produire de meilleures recommandations. Notre approche suppose que les préférences de l'utilisateur changent selon le contexte d'utilisation. Par exemple, un utilisateur écoute un genre de musique en conduisant vers son travail, un autre type en voyageant avec la famille en vacances, un autre pendant une soirée romantique ou aux fêtes. De plus, si la sélection a été générée pour plus d'un utilisateur (voyage en famille, fête) le système proposera des chansons en fonction des préférences de tous ces utilisateurs. L'objectif principal de notre système est de recommander à l'utilisateur de la musique à partir de sa collection personnelle ou à partir de la collection du système, les nouveautés et les prochains concerts. Un autre objectif de notre système sera de collecter des données provenant de sources extérieures, en s'appuyant sur des techniques de crawling et sur les flux RSS pour offrir des informations reliées à la musique tels que: les nouveautés, les prochains concerts, les paroles et les artistes similaires. Nous essayerons d’unifier des ensembles de données disponibles gratuitement sur le Web tels que les habitudes d’écoute de Last.fm, la base de données de la musique de MusicBrainz et les étiquettes des MusicStrands afin d'obtenir des identificateurs uniques pour les chansons, les albums et les artistes.

Who influence the music tastes of adolescents? A study on interpersonal influence in social networks

Research on music information behavior demonstrates that people rely primarily on others to discover new music. This paper reports on a qualitative study aiming at exploring more in-depth how music information circulates within the social networks of late adolescents and the role the different people involved in the process play. In-depth interviews were conducted with 19 adolescents (15-17 years old). The analysis revealed that music opinion leaders showed eagerness to share music information, tended to seek music information on an ongoing basis, and were perceived as being more knowledgeable than others in music. It was found that the ties that connected participants to opinion leaders were predominantly strong ties, which suggests that trustworthiness is an important component of credibility. These findings could potentially help identify new avenues for the improvement of music recommender systems.

Modelling the Human Values Scale from Consumers Transactional Data Bases

The main objective of this paper aims at developing a methodology that takes into account the human factor extracted from the data base used by the recommender systems, and which allow to resolve the specific problems of prediction and recommendation. In this work, we propose to extract the user's human values scale from the data base of the users, to improve their suitability in open environments, such as the recommender systems. For this purpose, the methodology is applied with the data of the user after interacting with the system. The methodology is exemplified with a case study

Adaptation and Personalisation

(with notes in powerpoint)

Methodology to obtain the user's Human Values Scale from Smart User Models

En años recientes,la Inteligencia Artificial ha contribuido a resolver problemas encontrados en el desempeño de las tareas de unidades informáticas, tanto si las computadoras están distribuidas para interactuar entre ellas o en cualquier entorno (Inteligencia Artificial Distribuida). Las Tecnologías de la Información permiten la creación de soluciones novedosas para problemas específicos mediante la aplicación de los hallazgos en diversas áreas de investigación. Nuestro trabajo está dirigido a la creación de modelos de usuario mediante un enfoque multidisciplinario en los cuales se emplean los principios de la psicología, inteligencia artificial distribuida, y el aprendizaje automático para crear modelos de usuario en entornos abiertos; uno de estos es la Inteligencia Ambiental basada en Modelos de Usuario con funciones de aprendizaje incremental y distribuido (conocidos como Smart User Model). Basándonos en estos modelos de usuario, dirigimos esta investigación a la adquisición de características del usuario importantes y que determinan la escala de valores dominantes de este en aquellos temas en los cuales está más interesado, desarrollando una metodología para obtener la Escala de Valores Humanos del usuario con respecto a sus características objetivas, subjetivas y emocionales (particularmente en Sistemas de Recomendación).Una de las áreas que ha sido poco investigada es la inclusión de la escala de valores humanos en los sistemas de información. Un Sistema de Recomendación, Modelo de usuario o Sistemas de Información, solo toman en cuenta las preferencias y emociones del usuario [Velásquez, 1996, 1997; Goldspink, 2000; Conte and Paolucci, 2001; Urban and Schmidt, 2001; Dal Forno and Merlone, 2001, 2002; Berkovsky et al., 2007c]. Por lo tanto, el principal enfoque de nuestra investigación está basado en la creación de una metodología que permita la generación de una escala de valores humanos para el usuario desde el modelo de usuario. Presentamos resultados obtenidos de un estudio de casos utilizando las características objetivas, subjetivas y emocionales en las áreas de servicios bancarios y de restaurantes donde la metodología propuesta en esta investigación fue puesta a prueba.En esta tesis, las principales contribuciones son: El desarrollo de una metodología que, dado un modelo de usuario con atributos objetivos, subjetivos y emocionales, se obtenga la Escala de Valores Humanos del usuario. La metodología propuesta está basada en el uso de aplicaciones ya existentes, donde todas las conexiones entre usuarios, agentes y dominios que se caracterizan por estas particularidades y atributos; por lo tanto, no se requiere de un esfuerzo extra por parte del usuario.

Combining collaborative and content-based filtering to recommend research papers

The number of research papers available today is growing at a staggering rate, generating a huge amount of information that people cannot keep up with. According to a tendency indicated by the United States’ National Science Foundation, more than 10 million new papers will be published in the next 20 years. Because most of these papers will be available on the Web, this research focus on exploring issues on recommending research papers to users, in order to directly lead users to papers of their interest. Recommender systems are used to recommend items to users among a huge stream of available items, according to users’ interests. This research focuses on the two most prevalent techniques to date, namely Content-Based Filtering and Collaborative Filtering. The first explores the text of the paper itself, recommending items similar in content to the ones the user has rated in the past. The second explores the citation web existing among papers. As these two techniques have complementary advantages, we explored hybrid approaches to recommending research papers. We created standalone and hybrid versions of algorithms and evaluated them through both offline experiments on a database of 102,295 papers, and an online experiment with 110 users. Our results show that the two techniques can be successfully combined to recommend papers. The coverage is also increased at the level of 100% in the hybrid algorithms. In addition, we found that different algorithms are more suitable for recommending different kinds of papers. Finally, we verified that users’ research experience influences the way users perceive recommendations. In parallel, we found that there are no significant differences in recommending papers for users from different countries. However, our results showed that users’ interacting with a research paper Recommender Systems are much happier when the interface is presented in the user’s native language, regardless the language that the papers are written. Therefore, an interface should be tailored to the user’s mother language.

Sistema de recomendação de produtos online baseado em palavras-chave

This project aims to explore the many methods used for the development of recommendation systems to user ’ s items and apply the content - based recommendation method on a prototype system whose purpose is to recommend books to users. This paper exposes the most popular methods for creating systems capable of providing items (products) according to user preferences, such as collaborat ive filtering and content - based. It also point different techniques that can be applied to calculate the similarity between two entities, for items or users, as the Pearson ’s method, calculating the cosine of vectors and more recently, a proposal to use a Bayesian system under a Dirichlet distribution. In addition, this work has the purpose to go through various points on the design of an online application, or a website, dealing not only oriented algorithms issues, but also the definition of development to ols and techniques to improve the user’s experience. The tools used for the development of the page are listed, and a topic about web design is also discussed in order to emphasize the importance of the layout of the application. At the end, some examples of recommender systems are presented for curiosity , learning and research purposes

Groupme: city data and multi-preference activity allocation

Classic group recommender systems focus on providing suggestions for a fixed group of people. Our work tries to give an inside look at design- ing a new recommender system that is capable of making suggestions for a sequence of activities, dividing people in subgroups, in order to boost over- all group satisfaction. However, this idea increases problem complexity in more dimensions and creates great challenge to the algorithm’s performance. To understand the e↵ectiveness, due to the enhanced complexity and pre- cise problem solving, we implemented an experimental system from data collected from a variety of web services concerning the city of Paris. The sys- tem recommends activities to a group of users from two di↵erent approaches: Local Search and Constraint Programming. The general results show that the number of subgroups can significantly influence the Constraint Program- ming Approaches’s computational time and e�cacy. Generally, Local Search can find results much quicker than Constraint Programming. Over a lengthy period of time, Local Search performs better than Constraint Programming, with similar final results.

A collaborative filtering similarity measure based on singularities.

Recommender systems play an important role in reducing the negative impact of informa- tion overload on those websites where users have the possibility of voting for their prefer- ences on items. The most normal technique for dealing with the recommendation mechanism is to use collaborative filtering, in which it is essential to discover the most similar users to whom you desire to make recommendations. The hypothesis of this paper is that the results obtained by applying traditional similarities measures can be improved by taking contextual information, drawn from the entire body of users, and using it to cal- culate the singularity which exists, for each item, in the votes cast by each pair of users that you wish to compare. As such, the greater the measure of singularity result between the votes cast by two given users, the greater the impact this will have on the similarity. The results, tested on the Movielens, Netflix and FilmAffinity databases, corroborate the excellent behaviour of the singularity measure proposed.

A collaborative filtering approach to mitigate the new user cold start problem.

The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommenda- tions received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system?s collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neu- ral learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave- one-out cross validation.

A Balanced Memory-Based Collaborative Filtering Similarity Measure.

Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures.