Journal article
Live streaming recommendations based on dynamic representation learning
Decision Support Systems, Vol.169, 113957
06/2023
DOI: 10.1016/j.dss.2023.113957
Abstract
•Consider the dynamic nature of live streaming rooms and users currently in them.•Incorporate path of users in entering and departing from rooms into the model.•Compare the proposed model with state-of-the-art methods using real-world datasets.
As an emerging form of social media, live streaming services (e.g., Twitch and Clubhouse) allow users to interact with hosts and peers in real time while enjoying shows or participating in discussions. These platforms are also dynamic, with shows or discussions changing quickly inside a room and users frequently switching between rooms. To improve user engagement and experience on such platforms, we design a new recommendation model named Dynamic Representations for Live Streaming Rooms (DRIVER) to provide room recommendations. Guided by the Integrated Framework for Consumer Path Modeling and the social affordance theory, DRIVER infers dynamic representations of live streaming rooms by leveraging users’ behavior paths in entering, staying in, and leaving rooms. One contribution of our model is a new and efficient dynamic learning framework to model instantaneous and ever-changing inter-room relationships by considering individual users’ behavior paths after leaving a room. Also supported by social affordance theory, another methodological novelty of our model is to capture dynamic characteristics of a room by incorporating features of the current audience inside the room. Experiments on real-world datasets from two different types of live streaming platforms demonstrate that DRIVER outperforms state-of-the-art representation learning methods and sequential recommender systems. The proposed method also has implications for recommender system design in other contexts, in which items are characterized by users’ dynamic behavior paths and ongoing social interactions.
Details
- Title: Subtitle
- Live streaming recommendations based on dynamic representation learning
- Creators
- Ge Gao - Tsinghua UniversityHongyan Liu - Tsinghua UniversityKang Zhao - Tippie College of Business, The University of Iowa, United States
- Resource Type
- Journal article
- Publication Details
- Decision Support Systems, Vol.169, 113957
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.dss.2023.113957
- ISSN
- 0167-9236
- Language
- English
- Date published
- 06/2023
- Academic Unit
- Business Analytics
- Record Identifier
- 9984386358702771
Metrics
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