From Clicks to Cues

Exploring user behaviour as a language in music video consumption

Master thesis (2023)

Authors

V. Mittal Electrical Engineering, Mathematics and Computer Science

Contributors

Ujwal Gadiraju Web Information Systems - EEMCS (supervisor 1)
G.M. Allen Web Information Systems - EEMCS (supervisor 1)
M.S. Pera Web Information Systems - EEMCS (coach)
M. Khosla (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Vishruty Mittal
User behaviour Language modeling Cluster analysis
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Published Date

14-06-2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

As music video streaming occupies a significant market share in how people consume music, gaining an understanding of user behavioural patterns becomes increasingly crucial. This understanding can enable better music video streaming experiences by tailoring them towards more personalized and user-centric designs. Though prior works have emphasized user behaviour during solely listening to music, understanding user actions/clicks while consuming music videos remains largely unexplored. Given the unique experience offered by the combination of audio and visual elements, there is a need for focused research in this area.

Therefore this study attempts to bridge this research gap by collecting and analysing a large dataset of streaming sessions from a music video streaming company - XITE. In total, we analyzed 1.8 million sessions from approximately 270,000 unique users. The behaviour exhibited during those sessions is interpreted as a language and modelled using the Language Model - Doc2Vec. This facilitated the conversion of session action sequences into embeddings. Our findings suggest that music video streaming sessions exhibit cohesive user interaction patterns, which can be grouped into distinct clusters, thereby enabling the detection of distinct behavioural patterns across user sessions.


Furthermore, previous studies have indicated that user interactions with multimedia streaming platforms can be influenced by the context in which content is consumed. Extending these findings, our analysis of behavioural clusters revealed that certain user behaviours while consuming music videos are associated with specific music video genres and temporal factors. For instance, we discovered that passive sessions predominantly commence around 10 am, while sessions requiring more active engagement typically start in the evening. The insights derived from this study are valuable for improving user-centric design in music video streaming platforms and providing businesses with data-driven recommendations for strategic planning.