We study the statistical properties of the sampled scale-free networks, deeply related to the proper identification of various real-world networks. We exploit three methods of sampling and investigate the topological properties such as degree and betweenness centrality distribution, average path length, assortativity, and clustering coefficient of sampled networks compared with those of original networks. It is found that the quantities related to those properties in sampled networks appear to be estimated quite differently for each sampling method. We explain why such a biased estimation of quantities would emerge from the sampling procedure and give appropriate criteria for each sampling method to prevent the quantities from being overestimated or underestimated.

pdf/statistical-properties-of-sampled-networks-7bp9b1ok4j.pdf

Statistical Properties of Sampled Networks

This chapter gives an introduction to music recommender systems research. We highlight the distinctive characteristics of music, as compared to other kinds of media. We then provide a literature survey of content-based music recommendation, contextual music recommendation, hybrid methods, and sequential music recommendation, followed by overview of evaluation strategies and commonly used data sets. We conclude by pointing to the most important challenges faced by music recommendation research.

Music Recommender Systems

Playlist generation is an important task in music information retrieval. While previous work has treated a playlist collection as an undifferentiated whole, we propose to build playlist models which are tuned to specific categories or dialects of playlists. Toward this end, we develop a general class of flexible and scalable playlist models based upon hypergraph random walks. To evaluate the proposed models, we present a large corpus of categorically annotated, user-generated playlists. Experimental results indicate that category-specific models can provide substantial improvements in accuracy over global playlist models.

/pdf/hypergraph-models-of-playlist-dialects-5acbx4lmsk.pdf

Hypergraph models of playlist dialects

Context aware recommender systems go beyond the traditional personalized recommendation models by incorporating a form of situational awareness. They provide recommendations that not only correspond to a user's preference profile, but that are also tailored to a given situation or context. We consider the setting in which contextual information is represented as a subset of an item feature space describing short-term interests or needs of a user in a given situation. This contextual information can be provided by the user in the form of an explicit query, or derived implicitly.We propose a unified probabilistic model that integrates user profiles, item representations, and contextual information. The resulting recommendation framework computes the conditional probability of each item given the user profile and the additional context. These probabilities are used as recommendation scores for ranking items. Our model is an extension of the Latent Dirichlet Allocation (LDA) model that provides the capability for joint modeling of users, items, and the meta-data associated with contexts. Each user profile is modeled as a mixture of the latent topics. The discovered latent topics enable our system to handle missing data in item features. We demonstrate the application of our framework for article and music recommendation. In the latter case, the set of popular tags from social tagging Web sites are used for context descriptions. Our evaluation results show that considering context can help improve the quality of recommendations.

Query-driven context aware recommendation

Nowadays, thanks to the popularization of music streaming services, we gained access to millions of songs to listen to. One of the methods employed by these services to support browsing and promote song discovery are playlists. Additionally, creating and sharing playlists over the Internet have become common practices. A playlist can be defined as a “sequence of songs meant to be listened to as a group”. Research on playlist creation has been done according to three perspectives: i) manual creation; ii) automatic generation and recommendation; and iii) assisted playlist creation. In this paper we review previous research on these three approaches, which we believe are complementary on the subject of playlist creation. We highlight the importance of combining insights from these three perspectives to better understand the current problems and methods, criteria and techniques, and how they complement each other. Furthermore, we identify promising research directions for the three different approaches of playlist creation.

From manual to assisted playlist creation: a survey

It is not hyperbole to note that a revolution has occurred in the way that we as a society distribute data and information. This revolution has come about through the confluence of Web-related technologies and the approaching universal adoption of internet connectivity. Add to this mix the normalised use of lossy compression in digital music and the increase in digital music download and streaming services; the result is an environment where nearly anyone can listen to nearly any piece of music nearly anywhere. This is in many respects the pinnacle in music access and availability. Yet, a listener is now faced with a
dilemma of choice. Without being familiar with the ever-expanding millions of songs available, how does a listener know what to listen to? If a near-complete collection of recorded music is available what does one listen to next? While the world of music distribution underwent a revolution, the ubiquitous access and availability it created brought new problems in recommendation and discovery.

In this thesis, a solution to these problems of recommendation and discovery is presented. We begin with an introduction to the core concepts around the playlist (i.e. sequential ordering of musical works). Next, we examine the history of the playlist as a recommendation technique, starting from before the invention of audio recording and moving through to modern automatic methods. This leads to an awareness that the creation of suitable playlists requires a high degree of knowledge of the relation between songs in a collection (e.g. song similarity). To better inform our base of knowledge of the relationships between songs we explore the use of social network analysis in combination with content-based music information retrieval. In an effort to show the promise of this more complex relational space, a fully automatic interactive radio system is proposed, using audio-content and social network data as a backbone. The implementation of the system is detailed. The creation of this system presents another problem in the area of evaluation. To that end, a novel distance metric between playlists is specified and tested. We then conclude with a discussion of what has been shown and what future work remains.

Contextualize Your Listening: The Playlist as Recommendation Engine

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Contextualize Your Listening: The Playlist as Recommendation Engine