Temporal influence over the Last.fm social network

In a previous result, we showed that the influence of social contacts spreads information about new artists through the Last.fm social network. We successfully decomposed influence from effects of trends, global popularity, and homophily or shared environment of friends. In this paper, we present our new experiments that use a mathematically sound formula for defining and measuring the influence in the network. We provide new baseline and influence models and evaluation measures, both batch and online, for real-time recommendations with very strong temporal aspects. Our experiments are carried over the 2-year “scrobble” history of 70,000 Last.fm users. In our results, we formally define and distil the effect of social influence. In addition, we provide new models and evaluation measures for real-time recommendations with very strong temporal aspects. © 2015, Springer-Verlag Wien

To what extent homophily and influencer networks explain song popularity

Forecasting the popularity of new songs has become a standard practice in the music industry and provides a comparative advantage for those that do it well. Considerable efforts were put into machine learning prediction models for that purpose. It is known that in these models, relevant predictive parameters include intrinsic lyrical and acoustic characteristics, extrinsic factors (e.g., publisher influence and support), and the previous popularity of the artists. Much less attention was given to the social components of the spreading of song popularity. Recently, evidence for musical homophily - the tendency that people who are socially linked also share musical tastes - was reported. Here we determine how musical homophily can be used to predict song popularity. The study is based on an extensive dataset from the last.fm online music platform from which we can extract social links between listeners and their listening patterns. To quantify the importance of networks in the spreading of songs that eventually determines their popularity, we use musical homophily to design a predictive influence parameter and show that its inclusion in state-of-the-art machine learning models enhances predictions of song popularity. The influence parameter improves the prediction precision (TP/(TP+FN)) by about 50% from 0.14 to 0.21, indicating that the social component in the spreading of music plays at least as significant a role as the artist's popularity or the impact of the genre.Comment: 7 pages, 3 figure

Location-aware online learning for top-k recommendation

We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain. For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user-item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods. Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message. © 2016 Elsevier B.V

Understanding Streaming Music Diffusion in a Semi-Closed Social Environment

Music social networks play a role in the diffusion of music. There are different ways a piece of music reaches people in a network: through the influence of social connections or via the discovery of external information, such as mass media, newspapers, etc. This empirical study uses over 10 months of user listening data from a music social network to examine the effects of external information on streaming music diffusion at the macro- and micro-levels. The data include weekly listening records for 557,554 users. Our results suggest that external information is a significant driver of increased streaming music diffusion, in comparison to in- network influences. We also found evidence of variation in the different influences, such as for a scale effect, the validity and type of information shared, and the impact of geolocation. These insights can be used to promote music and design personalized music recommendations