# GTS-LUM: Reshaping User Behavior Modeling with LLMs in Telecommunications Industry

arXiv: https://arxiv.org/abs/2504.06511 · HTML: https://mlsanity.com/paper/2504.06511/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding](https://mlsanity.com/paper/1809.07426/) (2018)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [Recommender Systems with Generative Retrieval](https://mlsanity.com/paper/2305.05065/) (2023)
- [How to Index Item IDs for Recommendation Foundation Models](https://mlsanity.com/paper/2305.06569/) (2023)
- [NoteLLM: A Retrievable Large Language Model for Note Recommendation](https://mlsanity.com/paper/2403.01744/) (2024)
- [Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2408.09698/) (2024)
- [MMGRec: Multimodal Generative Recommendation with Transformer Model](https://mlsanity.com/paper/2404.16555/) (2024)
- [Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models](https://mlsanity.com/paper/2402.08670/) (2024)
- [Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph](https://mlsanity.com/paper/2402.13750/) (2024)
- [Towards Empathetic Conversational Recommender Systems](https://mlsanity.com/paper/2409.10527/) (2024)
- [Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization](https://mlsanity.com/paper/2412.13771/) (2024)
- [Modeling Dual Period-Varying Preferences for Takeaway Recommendation](https://mlsanity.com/paper/2306.04370/) (2023)

## Abstract (arXiv)

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.