GTS-LUM: Reshaping User Behavior Modeling with LLMs in Telecommunications Industry
This provides a domain-specific solution for telecommunication service providers to improve user behavior modeling for package design and marketing, though it is incremental as it builds on existing LLM and recommendation techniques.
The paper tackles the challenge of modeling long-term and periodic user behavior with diverse time granularities and multi-modal data in telecommunications by introducing GTS-LUM, an end-to-end framework that outperforms existing LLM4Rec approaches in experiments on industrial datasets.
As telecommunication service providers shifting their focus to analyzing user behavior for package design and marketing interventions, a critical challenge lies in developing a unified, end-to-end framework capable of modeling long-term and periodic user behavior sequences with diverse time granularities, multi-modal data inputs, and heterogeneous labels. This paper introduces GTS-LUM, a novel user behavior model that redefines modeling paradigms in telecommunication settings. GTS-LUM adopts a (multi-modal) encoder-adapter-LLM decoder architecture, enhanced with several telecom-specific innovations. Specifically, the model incorporates an advanced timestamp processing method to handle varying time granularities. It also supports multi-modal data inputs -- including structured tables and behavior co-occurrence graphs -- and aligns these with semantic information extracted by a tokenizer using a Q-former structure. Additionally, GTS-LUM integrates a front-placed target-aware mechanism to highlight historical behaviors most relevant to the target. Extensive experiments on industrial dataset validate the effectiveness of this end-to-end framework and also demonstrate that GTS-LUM outperforms LLM4Rec approaches which are popular in recommendation systems, offering an effective and generalizing solution for user behavior modeling in telecommunications.