In this demo paper we describe PlaylistCreator, an assisted approach for supporting the creation of music playlists. Our solution allows creators to express song selection and browsing through a visual representation of their intents in a unified view, which relies on a set-based model for representing sources of songs. Creators can convey their purposes by seamlessly combining criteria for song selection from either manual or automatic sources, such as artists and albums, or similarity measures between artists or songs.

Music recommendation systems based on Collaborative Filtering methods have been extensively developed over the last years. Typically, they work by analyzing the past user-song relationships, and provide informed guesses based on the overall information collected from other users. Although the music listening behavior is a repetitive and time-dependent process, these methods have not taken this into account and only consider user-song interaction for recommendation. In this work, we explore the usage of temporal context and session diversity in Session-based Collaborative Filtering techniques for music recommendation. We compared two techniques to capture the users' listening patterns over time: one explicitly extracts temporal properties and session diversity, to group and compare the similarity of sessions, the other uses a generative topic modeling algorithm, which is able to implicitly model temporal patterns. We evaluated the developed algorithms by measuring the Hit Ratio, and the Mean Reciprocal Rank. Results reveal that the inclusion of temporal information, either explicitly or implicitly, increases significantly the accuracy of the recommendation, while compared to the traditional session-based CF.

Over the past years, music listening histories have become easily accessible due to the expansion of online lifelogging services. These histories represent the sequence of songs listen by users over time. Although this data contains intrinsic users' tastes and listening behaviors, it has been mainly used to personalize recommendations. Tools to help users exploring and reasoning about the information contained in the listening history, only recently have started to emerge. In this paper we describe a new visualization and exploration tool that allows users to interactively browse their listening histories, while leading them to identify listening trends and habits. Our solution combines a rich-featured timeline-based visualization, a set of synchronized-views and an interactive filtering mechanism to provide a flexible, effective and easy to use system for the analysis and knowledge exploration of listening histories. This was complemented with brushing and highlighting techniques to uncover listening trends about artists, albums and songs. Experimental evaluation with users revealed that they were able to complete all the requested tasks with a low error rate, and that they found the solution flexible and easy to use. Additionally, users were able to infer about their main life events and listening changes, which indicates that our combination of visualization techniques is effective in conveying relevant information about the listening habits.

Blogs are widely used today to publish information on a regular basis. However, users have difficulty in exploring their content and in discovering relevant information. This is due, among other things, to blogs rigid structure, with very long pages, and to the lack of mechanisms for effective navigation and exploration. To overcome these problems, we developed an exploration tool, to help users navigate, browse and visualize blogs. It was developed based on the following four design principles i) a blog should provide an overview of its activity and content to help users identify publication patterns and relevant entries; ii) blogs should present rich compact representations of entries to help readers anticipate the content of posts; iii) comments should have a relevant role, since they convey the social component of the blog; iv) blogs should offer an interactive filtering mechanism to help users explore and find relevant posts. A comparative evaluation with users confirmed the validity of the design principles, since our solution was more efficient, effective and usable than the usual blog interface and the Google Reader.

Nowadays, people spend time using services to track their music listening history. Although these services provide statistics and small graphics/charts, they are mainly used to record and to allow direct access to the information, not providing any visualization and exploration functionality. In this paper we describe a new approach for browsing and visualizing music listening histories, which combines a timeline-based visualization, with a set of synchronized-views and an interactive filtering mechanism to provide a flexible and easy to use solution. This was complemented with brushing and highlighting techniques that allow users to observe trends on artists, albums and tracks listening. Experimental evaluation with users revealed that they were able to complete all the proposed tasks with a low error rate, and that they found the solution easy to use. Moreover, users liked our approach for browsing and exploring listening histories, emphasizing its flexibility and effectiveness, and founding the full experience engaging and rewarding.

In this paper we present MuVis, an interactive visualization and exploration tool for large music collections, based on music content and metadata. We combined a user-centered design with three main components: information visualization techniques (based on semantic ordered treemaps), music information retrieval mechanisms (for semantic and content-based information extraction) and dynamic queries, to offer users a more efficient, flexible and yet, easy to use solution for browsing music collections and to create playlists. Preliminary results reveal that our solution is faster and easier to use than the Windows Media Player, allowing users to perform a more effective and fast navigation, while getting a deeper knowledge of their library. Satisfaction survey revealed that users liked our approach for browsing, filtering and creating playlists, while at the same time they were able to "re-discover" forgotten music, due to the similarity mechanisms incorporated in our solution.