A hybrid approach for item collection recommendations : an application to automatic playlist continuation

Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are rarely consumed in isolation, users would rather need a set of items, designed to work well together, while having some cognitive properties as a whole, related to their perception of quality and satisfaction. In this thesis, a hybrid case-based recommendation approach for item collections is proposed. In particular, an application to automatic playlist continuation, addressing similar cognitive concepts, rather than similar users, is presented. Playlists, that are sets of music items designed to be consumed as a sequence, with a specific purpose and within a specific context, are treated as cases. The proposed recommender system is based on a meta-level hybridization. First, Latent Dirichlet Allocation is applied to the set of past playlists, described as distributions over music styles, to identify their underlying concepts. Then, for a started playlist, its semantic characteristics, like its latent concept and the styles of the included items, are inferred, and Case-Based Reasoning is applied to the set of past playlists addressing the same concept, to construct and recommend a relevant playlist continuation. A graph-based item model is used to overcome the semantic gap between songs’ signal-based descriptions and users’ high-level preferences, efficiently capture the playlists’ structures and the similarity of the music items in those. As the proposed method bases its reasoning on previous playlists, it does not require the construction of complex user profiles to generate accurate recommendations. Furthermore, apart from relevance, support to parameters beyond accuracy, like increased coherence or support to diverse items is provided to deliver a more complete user experience. Experiments on real music datasets have revealed improved results, compared to other state of the art techniques, while achieving a “good trade-off” between recommendations’ relevance, diversity and coherence. Finally, although actually focusing on playlist continuations, the designed approach could be easily adapted to serve other recommendation domains with similar characteristics.Los sistemas de recomendación actuales tienen como objetivo principal generar recomendaciones precisas de artículos, sin evaluar propiamente las múltiples dimensiones del problema de recomendación. Sin embargo, en dominios como la música, donde los artículos rara vez se consumen en forma aislada, los usuarios más bien necesitarían recibir recomendaciones de conjuntos de elementos, diseñados para que se complementaran bien juntos, mientras se cubran algunas propiedades cognitivas, relacionadas con su percepción de calidad y satisfacción. En esta tesis, se propone un sistema híbrido de recomendación meta-nivel, que genera recomendaciones de colecciones de artículos. En particular, el sistema se centra en la generación automática de continuaciones de listas de música, tratando conceptos cognitivos similares, en lugar de usuarios similares. Las listas de reproducción son conjuntos de elementos musicales diseñados para ser consumidos en secuencia, con un propósito específico y dentro de un contexto específico. El sistema propuesto primero aplica el método de Latent Dirichlet Allocation a las listas de reproducción, que se describen como distribuciones sobre estilos musicales, para identificar sus conceptos. Cuando se ha iniciado una nueva lista, se deducen sus características semánticas, como su concepto y los estilos de los elementos incluidos en ella. A continuación, el sistema aplica razonamiento basado en casos, utilizando las listas del mismo concepto, para construir y recomendar una continuación relevante. Se utiliza un grafo que modeliza las relaciones de los elementos, para superar el ?salto semántico? existente entre las descripciones de las canciones, normalmente basadas en características sonoras, y las preferencias de los usuarios, expresadas en características de alto nivel. También se utiliza para calcular la similitud de los elementos musicales y para capturar la estructura de las listas de dichos elementos. Como el método propuesto basa su razonamiento en las listas de reproducción y no en usuarios que las construyeron, no se requiere la construcción de perfiles de usuarios complejos para poder generar recomendaciones precisas. Aparte de la relevancia de las recomendaciones, el sistema tiene en cuenta parámetros más allá de la precisión, como mayor coherencia o soporte a la diversidad de los elementos para enriquecer la experiencia del usuario. Los experimentos realizados en bases de datos reales, han revelado mejores resultados, en comparación con las técnicas utilizadas normalmente. Al mismo tiempo, el algoritmo propuesto logra un "buen equilibrio" entre la relevancia, la diversidad y la coherencia de las recomendaciones generadas. Finalmente, aunque la metodología presentada se centra en la recomendación de continuaciones de listas de reproducción musical, el sistema se puede adaptar fácilmente a otros dominios con características similares.Postprint (published version

A hybrid recommender system for improving automatic playlist continuation

Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.This work has been partially supported by the Catalan Agency for Management of University and Research Grants (AGAUR) (2017 SGR 574), by the European Regional Development Fund (ERDF), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program –COMPETE 2020– (POCI-01-0145-FEDER006961), and by the Portuguese Foundation for Science and Technology (FCT) (UID/EEA/50014/2013).Peer ReviewedPostprint (author's final draft

A switching multi-level method for the long tail recommendation problem

Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users’ rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail

Using contextual information in music playlist recommendations

Recommender Systems have become a fundamental part of various applications supporting users when searching for items they could be interested in, at a given moment. However, the majority of Recommender Systems generate isolate item recommendations based mainly on user-item interactions, without taking into account other important information about the recommendation moment, able to deliver users a more complete experience. In this paper, a hybrid Case-based Reasoning model generating recommendations of sets of music items, based on the underlying structures found in previous playlists, is proposed. Furthermore, the described system takes into account the similarity of the basic contextual information of the current and the past recommendation moments. The initial evaluation shows that the proposed approach may deliver recommendations of equal and higher accuracy than some of the widely used techniquePeer ReviewedPostprint (author's final draft

Using contextual information in music playlist recommendations

Recommender Systems have become a fundamental part of various applications supporting users when searching for items they could be interested in,at a given moment. However, the majority of Recommender Systems generate isolate item recommendations based mainly on user-item interactions, without taking into account other important information about the recommendation moment, able to deliver users a more complete experience. In this paper, a hybrid Case-based Reasoning model generating recommendations of sets of music items, based on the underlying structures found in previous playlists, is proposed. Furthermore, the described system takes into account the similarity of the basic contextual information of the current and the past recommendation moments. The initial evaluation shows that the proposed approach may deliver recommendations of equal and higher accuracy than some of the widely used techniques.Peer ReviewedPostprint (author's final draft

Promoting Social Media Dissemination of Digital Images Through CBR-Based Tag Recommendation

Multimedia content has become an essential tool to share knowledge, sell products or disseminate messages. Some social networks use multimedia content to promote information and create social communities. In order to increase the impact of the digital content, those images or videos are labeled with different words, denominated tags. In this paper, we propose a recommender system which analyzes multimedia content and suggests tags to maximize its influence in the social community. It implements a Case-Based Reasoning architecture (CBR), which allows to learn from previous tagged content. The system has been evaluated through cross fold validation with a training and validation sets carefully constructed and extracted from Instagram. The results demonstrate that the system can suggest good options to label our image and maximize the influence of the multimedia content

Personalized Finance Advisory through Case-based Recommender Systems and Diversification Strategies

Recommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield

Music recommendation: audio neighbourhoods to discover music in the long tail.

Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already know, newly released tracks, and the more niche music found in the 'long tail' of on-line music. Tag-based recommenders are not effective in this 'long tail' because relatively few people are listening to these tracks and so tagging tends to be sparse. However, similarity neighbourhoods in audio space can provide additional tag knowledge that is useful to augment sparse tagging. A new recommender exploits the combined knowledge, from audio and tagging, using a hybrid representation that extends the track's tag-based representation by adding semantic knowledge extracted from the tags of similar music tracks. A user evaluation and a larger experiment using Last.fm user data both show that the new hybrid recommender provides better quality recommendations than using only tags, together with a higher level of discovery of unknown and niche music. This approach of augmenting the representation for items that have missing information, with corresponding information from similar items in a complementary space, offers opportunities beyond content-based music recommendation

Evaluation of unidirectional background push content download services for the delivery of television programs

Este trabajo de tesis presenta los servicios de descarga de contenido en modo push como un mecanismo eficiente para el envío de contenido de televisión pre-producido sobre redes de difusión. Hoy en día, los operadores de red dedican una cantidad considerable de recursos de red a la entrega en vivo de contenido televisivo, tanto sobre redes de difusión como sobre conexiones unidireccionales. Esta oferta de servicios responde únicamente a requisitos comerciales: disponer de los contenidos televisivos en cualquier momento y lugar. Sin embargo, desde un punto de vista estrictamente académico, el envío en vivo es únicamente un requerimiento para el contenido en vivo, no para contenidos que ya han sido producidos con anterioridad a su emisión. Más aún, la difusión es solo eficiente cuando el contenido es suficientemente popular. Los servicios bajo estudio en esta tesis utilizan capacidad residual en redes de difusión para enviar contenido pre-producido para que se almacene en los equipos de usuario. La propuesta se justifica únicamente por su eficiencia. Por un lado, genera valor de recursos de red que no se aprovecharían de otra manera. Por otro lado, realiza la entrega de contenidos pre-producidos y populares de la manera más eficiente: sobre servicios de descarga de contenidos en difusión. Los resultados incluyen modelos para la popularidad y la duración de contenidos, valiosos para cualquier trabajo de investigación basados en la entrega de contenidos televisivos. Además, la tesis evalúa la capacidad residual disponible en redes de difusión, por medio de estudios empíricos. Después, estos resultados son utilizados en simulaciones que evalúan las prestaciones de los servicios propuestos en escenarios diferentes y para aplicaciones diferentes. La evaluación demuestra que este tipo de servicios son un recurso muy útil para la entrega de contenido televisivo.This thesis dissertation presents background push Content Download Services as an efficient mechanism to deliver pre-produced television content through existing broadcast networks. Nowadays, network operators dedicate a considerable amount of network resources to live streaming live, through both broadcast and unicast connections. This service offering responds solely to commercial requirements: Content must be available anytime and anywhere. However, from a strictly academic point of view, live streaming is only a requirement for live content and not for pre-produced content. Moreover, broadcasting is only efficient when the content is sufficiently popular. The services under study in this thesis use residual capacity in broadcast networks to push popular, pre-produced content to storage capacity in customer premises equipment. The proposal responds only to efficiency requirements. On one hand, it creates value from network resources otherwise unused. On the other hand, it delivers popular pre-produced content in the most efficient way: through broadcast download services. The results include models for the popularity and the duration of television content, valuable for any research work dealing with file-based delivery of television content. Later, the thesis evaluates the residual capacity available in broadcast networks through empirical studies. These results are used in simulations to evaluate the performance of background push content download services in different scenarios and for different applications. The evaluation proves that this kind of services can become a great asset for the delivery of television contentFraile Gil, F. (2013). Evaluation of unidirectional background push content download services for the delivery of television programs [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31656TESI