English

Abstract

Recommender Systems provide suggestions for items (e.g., movies or songs) to be of use to a user. They must take into account information to deliver more useful (perceived) recommendations. Current music recommender takes an initial input of a song and plays music with similar characteristics, or music that other users have listened to along with the input song. Listening behaviors in terms of temporal information associated to ratings or playbacks are usually ignored. We propose a recommender that predicts the most rated songs that a given user is likely to play in the future analyzing and comparing user listening habits by means of signal processing techniques. Recommender systems provide suggestions based on user preferences in order to recommend items likely to be of interest to a user. It is obvious that user preferences are influenced by the current context, such as the current time of the day, mood, or current activities. Nevertheless, a few recommender systems explicitly include this information in the preference models. A special group of recommender systems are the ones based on the collaborative approach (Resnick et al., 1994; Shardanand and Maes, 1995; Breese et al., 1998). The system generates recommendations using only information about rating profiles for different users. Collaborative systems locate peer users with a rating history similar to the current user and generate recommendations using this neighborhood. Collaborative filtering (CF) systems have been successful in several recommender systems. The availability of large datasets and additional information that is easy collectable from the web, makes this task interesting. There are several issues that do not allow us to directly apply the traditional CF approach for music recommendation. The space of possible items (i.e., tracks) can be very large and, similarly, the user space can also be enormous. Often user ratings are not available or they cover only a small subset of the user library of songs. Moreover, when new users enter to the system or new songs are added to the global library, it is not possible to provide any recommendation to them due to the lack of any preference information (the so known cold-start problem). There is no chance to use taxonomies or ontologies to represent the new items and facilitate the clustering as happens in different domains (e.g., (Acampora et al., 2010a; Micarelli et al., 2009)) Content-based approaches collect information describing the items and then, based on the user preferences, they predict which tracks the user might enjoy (see for example the Pandora service1). The key component of this approach is the similarity function among the songs. Nevertheless, there is a strong limitation of the highlevel descriptors that can be automatically extracted from the tracks (Celma, 2010). One more relevant issue that traditional CF approaches do not take into consideration is the listening behavior of the user in terms of temporal information. The timestamp of an item (i.e., when the song song is played) is an important factor for the recommendation algorithm. Usually, the prediction function treats the older items as less relevant than the new ones, but any further reasoning about the temporal information is simply ignored. In this paper, we discuss a recommendation approach based on signal processing. In particular, a traditional CF approach is enhanced considering an improved similarity function between users. The user listening habits are represented by signals. Wavelet theory is used to study the related time-frequency representations of signals and draw similarity between listening behaviors. Signal processing techniques are not employed to extract features from the songs, but for representing and comparing those behaviors in or-