Embedding Emotional Context in Recommender Systems

Emotions are crucial for user's decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems

Recommender System Based on Consumer Product Reviews

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

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

Processament de música basat en el seu contingut

Projecte de recerca elaborat a partir d’una estada a la National University of Singapore Singapur, entre juliol i octubre del 2007. Donada l'explosió de la música a l'internet i la ràpida expansió de les col•leccions de música digital, un repte clau en l'àrea de la informació musical és el desenvolupament de sistemes de processament musical eficients i confiables. L'objectiu de la investigació proposada ha estat treballar en diferents aspectes de l'extracció, modelatge i processat del contingut musical. En particular, s’ha treballat en l'extracció, l'anàlisi i la manipulació de descriptors d'àudio de baix nivell, el modelatge de processos musicals, l'estudi i desenvolupament de tècniques d'aprenentatge automàtic per a processar àudio, i la identificació i extracció d'atributs musicals d'alt nivell. S’han revisat i millorat alguns components d'anàlisis d'àudio i revisat components per a l'extracció de descriptors inter-nota i intra-nota en enregistraments monofónics d'àudio. S’ha aplicat treball previ en Tempo a la formalització de diferents tasques musicals. Finalment, s’ha investigat el processat d'alt nivell de música basandonos en el seu contingut. Com exemple d'això, s’ha investigat com músics professionals expressen i comuniquen la seva interpretació del contingut musical i emocional de peces musicals, i hem usat aquesta informació per a identificar automàticament intèrprets. S’han estudiat les desviacions en paràmetres com to, temps, amplitud i timbre a nivell inter-nota i intra-nota.

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

Plataforma eMovie : xarxa social de pel·lícules

En aquest treball s'han estudiat diferents sistemes de recomanació, i s'han utilitzat per a veure com podrien arribar a recomanar pel·lícules als usuaris. Això s'ha realitzat dins del context de la recomanació web, un sistema mitjançant el qual es poden obtenir moltes dades dels usuaris, però que per altra banda requereix de temps de resposta ràpids.

Towards an emotion-driven interactive music system: ridging the gaps between affect, physiology and sound generation

Report for the scientific sojourn at the Stanford University from January until June 2007. Music is well known for affecting human emotional states, yet the relationship between specific musical parameters and emotional responses is still not clear. With the advent of new human-computer interaction (HCI) technologies, it is now possible to derive emotion-related information from physiological data and use it as an input to interactive music systems. Providing such implicit musical HCI will be highly relevant for a number of applications including music therapy, diagnosis, nteractive gaming, and physiologically-based musical instruments. A key question in such physiology-based compositions is how sound synthesis parameters can be mapped to emotional states of valence and arousal. We used both verbal and heart rate responses to evaluate the affective power of five musical parameters. Our results show that a significant correlation exists between heart rate and the subjective evaluation of well-defined musical parameters. Brightness and loudness showed to be arousing parameters on subjective scale while harmonicity and even partial attenuation factor resulted in heart rate changes typically associated to valence. This demonstrates that a rational approach to designing emotion-driven music systems for our public installations and music therapy applications is possible.

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

Predictability of music descriptor time series and its application to cover song detection

Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.

Using music as a signal for biofeedback

Studies on the potential benefits of conveying biofeedback stimulus using a musical signal have appeared in recent years with the intent of harnessing the strong effects that music listening may have on subjects. While results are encouraging, the fundamental question has yet to be addressed, of how combined music and biofeedback compares to the already established use of either of these elements separately. This experiment, involving young adults (N = 24), compared the effectiveness at modulating participants' states of physiological arousal of each of the following conditions: A) listening to pre-recorded music, B) sonification biofeedback of the heart rate, and C) an algorithmically modulated musical feedback signal conveying the subject's heart rate. Our hypothesis was that each of the conditions (A), (B) and (C) would differ from the other two in the extent to which it enables participants to increase and decrease their state of physiological arousal, with (C) being more effective than (B), and both more than (A). Several physiological measures and qualitative responses were recorded and analyzed. Results show that using musical biofeedback allowed participants to modulate their state of physiological arousal at least equally well as sonification biofeedback, and much better than just listening to music, as reflected in their heart rate measurements, controlling for respiration-rate. Our findings indicate that the known effects of music in modulating arousal can therefore be beneficially harnessed when designing a biofeedback protocol.

Plasticity in the sensorimotor cortex induced by Music-supported therapy in stroke patients: a TMS study.

Playing a musical instrument demands the engagement of different neural systems. Recent studies about the musician"s brain and musical training highlight that this activity requires the close interaction between motor and somatosensory systems. Moreover, neuroplastic changes have been reported in motor-related areas after short and long-term musical training. Because of its capacity to promote neuroplastic changes, music has been used in the context of stroke neurorehabilitation. The majority of patients suffering from a stroke have motor impairments, preventing them to live independently. Thus, there is an increasing demand for effective restorative interventions for neurological deficits. Music-supported Therapy (MST) has been recently developed to restore motor deficits. We report data of a selected sample of stroke patients who have been enrolled in a MST program (1 month intense music learning). Prior to and after the therapy, patients were evaluated with different behavioral motor tests. Transcranial Magnetic Stimulation (TMS) was applied to evaluate changes in the sensorimotor representations underlying the motor gains observed. Several parameters of excitability of the motor cortex were assessed as well as the cortical somatotopic representation of a muscle in the affected hand. Our results revealed that participants obtained significant motor improvements in the paretic hand and those changes were accompanied by changes in the excitability of the motor cortex. Thus, MST leads to neuroplastic changes in the motor cortex of stroke patients which may explain its efficacy.

Plasticity in the sensorimotor cortex induced by Music-supported therapy in stroke patients: a TMS study.

Playing a musical instrument demands the engagement of different neural systems. Recent studies about the musician"s brain and musical training highlight that this activity requires the close interaction between motor and somatosensory systems. Moreover, neuroplastic changes have been reported in motor-related areas after short and long-term musical training. Because of its capacity to promote neuroplastic changes, music has been used in the context of stroke neurorehabilitation. The majority of patients suffering from a stroke have motor impairments, preventing them to live independently. Thus, there is an increasing demand for effective restorative interventions for neurological deficits. Music-supported Therapy (MST) has been recently developed to restore motor deficits. We report data of a selected sample of stroke patients who have been enrolled in a MST program (1 month intense music learning). Prior to and after the therapy, patients were evaluated with different behavioral motor tests. Transcranial Magnetic Stimulation (TMS) was applied to evaluate changes in the sensorimotor representations underlying the motor gains observed. Several parameters of excitability of the motor cortex were assessed as well as the cortical somatotopic representation of a muscle in the affected hand. Our results revealed that participants obtained significant motor improvements in the paretic hand and those changes were accompanied by changes in the excitability of the motor cortex. Thus, MST leads to neuroplastic changes in the motor cortex of stroke patients which may explain its efficacy.

Plasticity in the sensorimotor cortex induced by Music-supported therapy in stroke patients: a TMS study.

Playing a musical instrument demands the engagement of different neural systems. Recent studies about the musician"s brain and musical training highlight that this activity requires the close interaction between motor and somatosensory systems. Moreover, neuroplastic changes have been reported in motor-related areas after short and long-term musical training. Because of its capacity to promote neuroplastic changes, music has been used in the context of stroke neurorehabilitation. The majority of patients suffering from a stroke have motor impairments, preventing them to live independently. Thus, there is an increasing demand for effective restorative interventions for neurological deficits. Music-supported Therapy (MST) has been recently developed to restore motor deficits. We report data of a selected sample of stroke patients who have been enrolled in a MST program (1 month intense music learning). Prior to and after the therapy, patients were evaluated with different behavioral motor tests. Transcranial Magnetic Stimulation (TMS) was applied to evaluate changes in the sensorimotor representations underlying the motor gains observed. Several parameters of excitability of the motor cortex were assessed as well as the cortical somatotopic representation of a muscle in the affected hand. Our results revealed that participants obtained significant motor improvements in the paretic hand and those changes were accompanied by changes in the excitability of the motor cortex. Thus, MST leads to neuroplastic changes in the motor cortex of stroke patients which may explain its efficacy.

Using music as a signal for biofeedback

Studies on the potential benefits of conveying biofeedback stimulus using a musical signal have appeared in recent years with the intent of harnessing the strong effects that music listening may have on subjects. While results are encouraging, the fundamental question has yet to be addressed, of how combined music and biofeedback compares to the already established use of either of these elements separately. This experiment, involving young adults (N = 24), compared the effectiveness at modulating participants' states of physiological arousal of each of the following conditions: A) listening to pre-recorded music, B) sonification biofeedback of the heart rate, and C) an algorithmically modulated musical feedback signal conveying the subject's heart rate. Our hypothesis was that each of the conditions (A), (B) and (C) would differ from the other two in the extent to which it enables participants to increase and decrease their state of physiological arousal, with (C) being more effective than (B), and both more than (A). Several physiological measures and qualitative responses were recorded and analyzed. Results show that using musical biofeedback allowed participants to modulate their state of physiological arousal at least equally well as sonification biofeedback, and much better than just listening to music, as reflected in their heart rate measurements, controlling for respiration-rate. Our findings indicate that the known effects of music in modulating arousal can therefore be beneficially harnessed when designing a biofeedback protocol.