A study on contextual influences on automatic playlist continuation

Recommender systems still mainly base their reasoning on pairwise interactions or information on individual entities, like item attributes or ratings, without properly evaluating the multiple dimensions of the recommendation problem. However, in many cases, like in music, items are rarely consumed in isolation, thus users rather need a set of items, selected to work well together, serving a specific purpose, while having some cognitive properties as a whole, related to their perception of quality and satisfaction, under given circumstances. In this paper, we introduce the term of playlist concept in order to capture the implicit characteristics of joint music item selections, related to their context, scope and general perception by the users. Although playlist consumptions may be associated with contextual attributes, these may be of various types, differently influencing users' preferences, based on their character and emotional state, therefore differently reflected on their final selections. We highlight on the use of this term in HybA, our hybrid recommender system, to identify clusters of similar playlists able to capture inherit characteristics and semantic properties, not explicitly described in them. The experimental results presented, show that this conceptual clustering results in playlist continuations of improved quality, compared to using explicit contextual parameters, or the commonly used collaborative filtering technique.Peer ReviewedPostprint (published version

Two-Sided Value-Based Music Artist Recommendation in Streaming Music Services

Most work on music recommendations has focused on the consumer side not the provider side. We develop a two-sided value-based approach to music artist recommendation for a streaming music scenario. It combines the value yielded for the music industry and consumers in an integrated model. For the industry, the approach aims to increase the conversion rate of potential listeners to adopters, which produces new revenue. For consumers, it aims to improve their utility related to recommendations they receive. We use one year of listening records for 15,000+ Last.fm users to train and test the proposed recommendation model on 143 artists. Compared to collaborative filtering, the results show some improvement in recommendation performance by considering both sides’ value in con-junction with other factors, including time, location, external information and listening behavior

Explorative Visual Analysis of Rap Music

Detecting references and similarities in music lyrics can be a difficult task. Crowdsourced knowledge platforms such as Genius. can help in this process through user-annotated information about the artist and the song but fail to include visualizations to help users find similarities and structures on a higher and more abstract level. We propose a prototype to compute similarities between rap artists based on word embedding of their lyrics crawled from Genius. Furthermore, the artists and their lyrics can be analyzed using an explorative visualization system applying multiple visualization methods to support domain-specific tasks

Generación de secuencias de obras de arte basado en vecindad y RNN

En los museos las obras de arte son distribuidas en un espacio físico por curadores. Dicha distribución no es causa y efecto del azar, sino buscando un delicado equilibrio entre emoción y razón. A partir de la digitalización y acceso masivo a las obras de arte, surge el interrogante de si es posible generar una secuencia automática de obras de artes de acuerdo a los intereses del espectador. Este trabajo busca definir el problema de la generación de secuencias de obras de arte. Se presentan dos enfoques que abordan la problemática haciendo énfasis en la organización intrínseca de la secuencia basadas en técnicas de vecindad y de Recurrent Neural Networks. Se entiende que esta perspectiva se acerca más al tipo de recomendación que haría un curador. Los enfoques son evaluados sobre un dataset que consiste de 52 recorridos definidos por los curadores del Museo del Prado y el Rijksmuseum. Si bien los resultados son preliminares, se observa que los tours predichos por ambos enfoques presentan semejanzas con los tours originales.Sociedad Argentina de Informátic

THREE ESSAYS ON COMPETITIVE STRATEGIES FOR DIGITAL PLATFORM BUSINESSES

As businesses in many industries adopt the platform business model, many aspects of the traditional business are going through a shake-up, including competition and strategies for gaining competitive advantage. When platforms are competing with each other, the network effects due to having a strong installed base create a strategic advantage and shape the competition. Additionally, another level of competition in the world of platforms is between complementors on a given platform which is also influenced by the presence of the network effects. In the three studies of this dissertation, we focus on competitive strategies for digital platform businesses. In the first essay, we look at competition between platforms and examine the emergence of Winners-Take-Some (WTS) market outcome in IT platform markets, where such markets are expected to yield a Winner-Takes-All (WTA) outcome. We use the cyclical video game console market as an appropriate context to investigate the influential factors in the market outcome in platform markets. We find a consistent increase in multi-homing among the most popular video-games that can pave the way for the emergence of WTS outcome. In the second essay, we are turning our focus to the strategies that platforms can adopt to improve emerging success metrics such as user engagement. We examine how digital content platforms can improve users’ engagement by providing popularity information signals. We evaluate the effect of conflicting and aligned information signals on users’ engagement in the context of music content platforms. We find that conflicting popularity information signals are more effective in increasing user engagement than the aligned popularity information signals. In the third essay and in the context of mobile app platforms, we focus on the competition between complementors. We study the role of app category characteristics on the performance of mobile app developers who offer apps in those categories and strive to gain competitive advantage. We evaluate category concentration and category popularity as two important factors and find that respectively, they negatively and positively influence new app’s performance for a given developer. We find that the negative effect of category concentration is stronger than the positive effect of category popularity