Intent-Satisfaction Modeling: From Music to Video Streaming

Abstract

Logged behavioral data is a common resource for enhancing the user experience on streaming platforms. In music streaming, Mehrotra et al. have shown how complementing behavioral data with user intent can help predict and explain user satisfaction. Do their findings extend to video streaming? Compared to music streaming, video streaming platforms provide relatively shallow catalogs. Finding the right content demands more active and conscious commitment from users than in the music streaming setting. Video streaming platforms, in particular, could thus benefit from a better understanding of user intents and satisfaction level. We replicate Mehrotra et al.’s study from music to video streaming and extend their modeling framework on two fronts: (i) improved modeling accuracy (random forests), and (ii) interpretability (Bayesian models). Like the original study, we find that user intent affects behavior and satisfaction itself, even if to a lesser degree, based on data analysis and modeling. By proposing a grouping of intents into decisive and explorative categories we highlight a tension: decisive video streamers are not as keen to interact with the user interface as exploration-seeking ones. Meanwhile, music streamers explore by listening. In this study, we find that in video streaming, unsatisfied users provide the main signal: intent influences satisfaction levels together with behavioral data, depending on our decisive vs. explorative grouping.