Brokerage Platform for Media Content Recommendation

Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.

A Multi–Agent Brokerage Platform for Media Content Recommendation

Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.

All About Portugal - Website dedicado ao turismo externo em Portugal

Grande parte do tráfego online tem origem em páginas de resultados de motores de de pesquisa. Estes constituem hoje uma ferramenta fundamental de que os turistas se socorrem para pesquisar e filtrar a informação necessária ao planeamento das suas viagens, sendo, por isso, bastante tidos em conta pelas entidades ligadas ao turismo no momento da definição das suas estratégias de marketing. No presente documento é descrita a investigação feita em torno do modo de funcionamento do motor de pesquisa Google e das métricas que utiliza para avaliação de websites e páginas web. Desta investigação resultou a implementação de um website de conteúdos afetos ao mercado de turismo e viagens em Portugal, focado no mercado do turismo externo – All About Portugal. A implementação do website pretende provar, sustentando-se em orientações da área do SEO, que a propagação de conteúdos baseada unicamente nos motores de pesquisa é viável, confirmando, deste modo, a sua importância. Os dados de utilização desse mesmo website introduzem novos elementos que poderão servir de base a novos estudos.

Food's visually perceived fat content affects discrimination speed in an orthogonal spatial task.

Choosing what to eat is a complex activity for humans. Determining a food's pleasantness requires us to combine information about what is available at a given time with knowledge of the food's palatability, texture, fat content, and other nutritional information. It has been suggested that humans may have an implicit knowledge of a food's fat content based on its appearance; Toepel et al. (Neuroimage 44:967-974, 2009) reported visual-evoked potential modulations after participants viewed images of high-energy, high-fat food (HF), as compared to viewing low-fat food (LF). In the present study, we investigated whether there are any immediate behavioural consequences of these modulations for human performance. HF, LF, or non-food (NF) images were used to exogenously direct participants' attention to either the left or the right. Next, participants made speeded elevation discrimination responses (up vs. down) to visual targets presented either above or below the midline (and at one of three stimulus onset asynchronies: 150, 300, or 450 ms). Participants responded significantly more rapidly following the presentation of a HF image than following the presentation of either LF or NF images, despite the fact that the identity of the images was entirely task-irrelevant. Similar results were found when comparing response speeds following images of high-carbohydrate (HC) food items to low-carbohydrate (LC) food items. These results support the view that people rapidly process (i.e. within a few hundred milliseconds) the fat/carbohydrate/energy value or, perhaps more generally, the pleasantness of food. Potentially as a result of HF/HC food items being more pleasant and thus having a higher incentive value, it seems as though seeing these foods results in a response readiness, or an overall alerting effect, in the human brain.

Trophic dynamics in the northern Humboldt Current system: insights from stable isotopes and stomach content analyses

The northern Humboldt Current system (NHCS) off Peru is one of the most productive world marine regions. It represents less than 0.1% of the world ocean surface but presently sustains about 10% of the world fish catch, with the Peruvian anchovy or anchoveta Engraulis ringens as emblematic fish resource. Compared with other eastern boundary upwelling systems, the higher fish productivity of the NHCS cannot be explained by a corresponding higher primary productivity. On another hand, the NHCS is the region where El Niño, and climate variability in general, is most notable. Also, surface oxygenated waters overlie an intense and extremely shallow Oxygen Minimum Zone (OMZ). In this context, the main objective of this study is to better understand the trophic flows in the NHCS using both stomach content and stable isotope analyses. The study focuses on a variety of organisms from low trophic levels such as zooplankton to top predators (seabirds and fur seals). The approach combines both long-term and specific studies on emblematic species such as anchoveta, and sardine Sardinops sagax and a more inclusive analysis considering the 'global' food web in the recent years (2008 – 2012) using stable isotope analysis. Revisiting anchovy and sardine we show that whereas phytoplankton largely dominated anchoveta and sardine diets in terms of numerical abundance, the carbon content of prey items indicated that zooplankton was by far the most important dietary component. Indeed for anchovy euphausiids contributed 67.5% of dietary carbon, followed by copepods (26.3%). Selecting the largest prey, the euphausiids, provide an energetic advantage for anchoveta in its ecosystem where oxygen depletion imposes strong metabolic constrain to pelagic fish. Sardine feed on smaller zooplankton than do anchoveta, with sardine diet consisting of smaller copepods and fewer euphausiids than anchoveta diet. Hence, trophic competition between sardine and anchovy in the northern Humboldt Current system is minimized by their partitioning of the zooplankton food resource based on prey size, as has been reported in other systems. These results suggest an ecological role for pelagic fish that challenges previous understanding of their position in the foodweb (zooplanktophagous instead of phytophagous), the functioning and the trophic models of the NHCS. Finally to obtain a more comprehensive vision of the relative trophic position of NHCS main components we used stable isotope analyses. For that purpose we analyzed the δ13C and δ15N stable isotope values of thirteen taxonomic categories collected off Peru from 2008 - 2011, i.e., zooplankton, fish, squids and air-breathing top predators. The δ15N isotope signature was strongly impacted by the species, the body length and the latitude. Along the Peruvian coast, the OMZ get more intense and shallow south of ~7.5ºS impacting the baseline nitrogen stable isotopes. Employing a linear mixed-effects modelling approach taking into account the latitudinal and body length effects, we provide a new vision of the relative trophic position of key ecosystem components. Also we confirm stomach content-based results on anchoveta Engraulis ringens and highlight the potential remarkable importance of an often neglected ecosystem component, the squat lobster Pleuroncodes monodon. Indeed, our results support the hypothesis according to which this species forage to some extent on fish eggs and larvae and can thus predate on the first life stages of exploited species. However, the δ13C values of these two species suggest that anchoveta and squat lobster do not exactly share the same habitat. This would potentially reduce some direct competition and/or predation.

QTL mapping for protein content in soybean cultivated in two tropical environments

The objectives of this study were to detect quantitative trait loci (QTL) for protein content in soybean grown in two distinct tropical environments and to build a genetic map for protein content. One hundred eighteen soybean recombinant inbred lines (RIL), obtained from a cross between cultivars BARC 8 and Garimpo, were used. The RIL were cultivated in two distinct Brazilian tropical environments: Cascavel county, in Paraná, and Viçosa county, in Minas Gerais (24º57'S, 53º27'W and 20º45'S, 42º52'W, respectively). Sixty-six SSR primer pairs and 65 RAPD primers were polymorphic and segregated at a 1:1 proportion. Thirty poorly saturated linkage groups were obtained, with 90 markers and 41 nonlinked markers. For the lines cultivated in Cascavel, three QTL were mapped in C2, E and N linkage groups, which explained 14.37, 10.31 and 7.34% of the phenotypic variation of protein content, respectively. For the lines cultivated in Viçosa, two QTL were mapped in linkage groups G and #1, which explained 9.51 and 7.34% of the phenotypic variation of protein content. Based on the mean of the two environments, two QTL were identified: one in the linkage group E (9.90%) and other in the group L (7.11%). In order for future studies to consistently detect QTL effects of different environments, genotypes with greater stability should be used.

Emotions in a Web-based Learning Environment

The aim of this thesis was to examine emotions in a web-based learning environment (WBLE). Theoretically, the thesis was grounded on the dimensional model of emotions. Four empirical studies were conducted. Study I focused on students’ anxiety and their self-efficacy in computer-using situations. Studies II and III examined the influence of experienced emotions on students’ collaborative visible and non-collaborative invisible activities and lurking in a WBLE. Study II also focused on the antecedents of the emotions students experience in a web-based learning environment. Study IV concentrated on clarifying the differences between emotions experienced in face-to-face and web-based collaborative learning. The results of these studies are reported in four original research articles published in scientific journals. The present studies demonstrate that emotions are important determinants of student behaviour in a web-based learning, and justify the conclusion that interactions on the web can and do have an emotional content. Based on the results of these empirical studies, it can be concluded that the emotions students experience during the web-based learning result mostly from the social interactions rather than from the technological context. The studies indicate that the technology itself is not the only antecedent of students’ emotional reactions in the collaborative web-based learning situations. However, the technology itself also exerted an influence on students’ behaviour. It was found that students’ computer anxiety was associated with their negative expectations of the consequences of using technology-based learning environments in their studies. Moreover, the results also indicated that student behaviours in a WBLE can be divided into three partially overlapping classes: i) collaborative visible ii) non-collaborative invisible activities, and iii) lurking. What is more, students’ emotions experienced during the web-based learning affected how actively they participated in such activities in the environment. Especially lurkers, i.e. students who seldom participated in discussions but frequently visited the online environment, experienced more negatively valenced emotions during the courses than did the other students. This result indicates that such negatively toned emotional experiences can make the lurking individuals less eager to participate in other WBLE courses in the future. Therefore, future research should also focus more precisely on the reasons that cause individuals to lurk in online learning groups, and the development of learning tasks that do not encourage or permit lurking or inactivity. Finally, the results from the study comparing emotional reactions in web-based and face-to-face collaborative learning indicated that the learning by means of web-based communication resulted in more affective reactivity when compared to learning in a face-to-face situation. The results imply that the students in the web-based learning group experienced more intense emotions than the students in the face-to-face learning group.The interpretations of this result are that the lack of means for expressing emotional reactions and perceiving others’ emotions increased the affectivity in the web-based learning groups. Such increased affective reactivity could, for example, debilitate individual’s learning performance, especially in complex learning tasks. Therefore, it is recommended that in the future more studies should be focused on the possibilities to express emotions in a text-based web environment to ensure better means for communicating emotions, and subsequently, possibly decrease the high level of affectivity. However, we do not yet know whether the use of means for communicating emotional expressions via the web (for example, “smileys” or “emoticons”) would be beneficial or disadvantageous in formal learning situations. Therefore, future studies should also focus on assessing how the use of such symbols as a means for expressing emotions in a text-based web environment would affect students’ and teachers’ behaviour and emotional state in web-based learning environments.

Privacy-Aware Peer-to- Peer Content Distribution Using Automatically Recombined Fingerprints

Peer-reviewed

Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Algorithmes de recommandation musicale

Ce mémoire est composé de trois articles qui s’unissent sous le thème de la recommandation musicale à grande échelle. Nous présentons d’abord une méthode pour effectuer des recommandations musicales en récoltant des étiquettes (tags) décrivant les items et en utilisant cette aura textuelle pour déterminer leur similarité. En plus d’effectuer des recommandations qui sont transparentes et personnalisables, notre méthode, basée sur le contenu, n’est pas victime des problèmes dont souffrent les systèmes de filtrage collaboratif, comme le problème du démarrage à froid (cold start problem). Nous présentons ensuite un algorithme d’apprentissage automatique qui applique des étiquettes à des chansons à partir d’attributs extraits de leur fichier audio. L’ensemble de données que nous utilisons est construit à partir d’une très grande quantité de données sociales provenant du site Last.fm. Nous présentons finalement un algorithme de génération automatique de liste d’écoute personnalisable qui apprend un espace de similarité musical à partir d’attributs audio extraits de chansons jouées dans des listes d’écoute de stations de radio commerciale. En plus d’utiliser cet espace de similarité, notre système prend aussi en compte un nuage d’étiquettes que l’utilisateur est en mesure de manipuler, ce qui lui permet de décrire de manière abstraite la sorte de musique qu’il désire écouter.

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.

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching.

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

Performance Study of an Improved Legendre Moment Descriptor as Region-Based Shape Descriptor

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision- recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications

Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Collaborative recommender agents based on case-based reasoning and trust

La comunitat científica que treballa en Intel·ligència Artificial (IA) ha dut a terme una gran quantitat de treball en com la IA pot ajudar a les persones a trobar el que volen dins d'Internet. La idea dels sistemes recomanadors ha estat extensament acceptada pels usuaris. La tasca principal d'un sistema recomanador és localitzar ítems, fonts d'informació i persones relacionades amb els interessos i preferències d'una persona o d'un grup de persones. Això comporta la construcció de models d'usuari i l'habilitat d'anticipar i predir les preferències de l'usuari. Aquesta tesi està focalitzada en l'estudi de tècniques d'IA que millorin el rendiment dels sistemes recomanadors. Inicialment, s'ha dut a terme un anàlisis detallat de l'actual estat de l'art en aquest camp. Aquest treball ha estat organitzat en forma de taxonomia on els sistemes recomanadors existents a Internet es classifiquen en 8 dimensions generals. Aquesta taxonomia ens aporta una base de coneixement indispensable pel disseny de la nostra proposta. El raonament basat en casos (CBR) és un paradigma per aprendre i raonar a partir de la experiència adequat per sistemes recomanadors degut als seus fonaments en el raonament humà. Aquesta tesi planteja una nova proposta de CBR aplicat al camp de la recomanació i un mecanisme d'oblit per perfils basats en casos que controla la rellevància i edat de les experiències passades. Els resultats experimentals demostren que aquesta proposta adapta millor els perfils als usuaris i soluciona el problema de la utilitat que pateixen el sistemes basats en CBR. Els sistemes recomanadors milloren espectacularment la qualitat dels resultats quan informació sobre els altres usuaris és utilitzada quan es recomana a un usuari concret. Aquesta tesi proposa l'agentificació dels sistemes recomanadors per tal de treure profit de propietats interessants dels agents com ara la proactivitat, la encapsulació o l'habilitat social. La col·laboració entre agents es realitza a partir del mètode de filtratge basat en la opinió i del mètode col·laboratiu de filtratge a partir de confiança. Els dos mètodes es basen en un model social de confiança que fa que els agents siguin menys vulnerables als altres quan col·laboren. Els resultats experimentals demostren que els agents recomanadors col·laboratius proposats milloren el rendiment del sistema mentre que preserven la privacitat de les dades personals de l'usuari. Finalment, aquesta tesi també proposa un procediment per avaluar sistemes recomanadors que permet la discussió científica dels resultats. Aquesta proposta simula el comportament dels usuaris al llarg del temps basat en perfils d'usuari reals. Esperem que aquesta metodologia d'avaluació contribueixi al progrés d'aquesta àrea de recerca.