User geospatial context for music recommendation in microblogs

Music information retrieval and music recommendation are seeing a paradigm shift towards methods that incorporate user context aspects. However, structured experiments on a standardized music dataset to investigate the effects of do-ing so are scarce. In this paper, we compare performance of various combinations of collaborative filtering and geospatial as well as cultural user models for the task of music recom-mendation. To this end, we propose a geospatial model that uses GPS coordinates and a cultural model that uses seman-tic locations (continent, country, and state of the user). We conduct experiments on a novel standardized music collec-tion, the “Million Musical Tweets Dataset ” of listing events extracted from microblogs. Overall, we find that modeling listeners ’ location via Gaussian mixture models and comput-ing similarities from these outperforms both cultural user models and collaborative filtering. Categories and Subject Descriptors Information systems [Information retrieval]: Music rec-ommendation; Human-centered computing [Collaborative and social computing]: Social medi

Learning to embed music and metadata for context-aware music recommendation

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. Contextual factors greatly influence users’ musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users’ contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users’ historical listening records. Specifically, the proposed approach first learns music pieces’ embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users’ global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user’s preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets

Sequence-based context-aware music recommendation

© 2017, Springer Science+Business Media, LLC. Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users’ music listening sequences using neural network models. Based on the learned representations, it then infers and models users’ general and contextual preferences for music from users’ historical listening records. Finally, music pieces in accordance with user’s preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Being connected to the local community through a festival mobile application

In this paper we report our investigation into how using and interacting with a local festival mobile app enhanced users’ festival experiences and connected them to other local users and their community. We explored the relationship between users’ perceived basic affordances of mobile technology, perceived opportunities of the festival app, and three elements that sustain the local community — attachment, engagement, and social support networks. Based on the usage logs of 348 active users, as well as survey responses from 80 users, we present a mobile-mediated local community framework and found that engagement is a key mediator of mobile experiences and facets of community

Impact of Listening Behavior on Music Recommendation.

The next generation of music recommendation systems willbe increasingly intelligent and likely take into account userbehavior for more personalized recommendations. In thiswork we consider user behavior when making recommendationswith features extracted from a user’s history of listeningevents. We investigate the impact of listener’s behaviorby considering features such as play counts, “mainstreaminess”,and diversity in music taste on the performanceof various music recommendation approaches. Theunderlying dataset has been collected by crawling socialmedia (specifically Twitter) for listening events. Each user’slistening behavior is characterized into a three dimensionalfeature space consisting of play count, “mainstreaminess”(i.e. the degree to which the observed user listens to currentlypopular artists), and diversity (i.e. the diversity ofgenres the observed user listens to). Drawing subsets ofthe 28,000 users in our dataset, according to these threedimensions, we evaluate whether these dimensions influencefigures of merit of various music recommendation approaches,in particular, collaborative filtering (CF) and CFenhanced by cultural information such as users located inthe same city or country

Tracking the History and Evolution of Entities: Entity-centric Temporal Analysis of Large Social Media Archives

How did the popularity of the Greek Prime Minister evolve in 2015? How did the predominant sentiment about him vary during that period? Were there any controversial sub-periods? What other entities were related to him during these periods? To answer these questions, one needs to analyze archived documents and data about the query entities, such as old news articles or social media archives. In particular, user-generated content posted in social networks, like Twitter and Facebook, can be seen as a comprehensive documentation of our society, and thus meaningful analysis methods over such archived data are of immense value for sociologists, historians and other interested parties who want to study the history and evolution of entities and events. To this end, in this paper we propose an entity-centric approach to analyze social media archives and we define measures that allow studying how entities were reflected in social media in different time periods and under different aspects, like popularity, attitude, controversiality, and connectedness with other entities. A case study using a large Twitter archive of four years illustrates the insights that can be gained by such an entity-centric and multi-aspect analysis.Comment: This is a preprint of an article accepted for publication in the International Journal on Digital Libraries (2018