Retrieval of Temporal Event Sequences from Textual Descriptions

2024-10-17

AI Summary

This work addresses text-driven Temporal Event Sequence Retrieval (TESR), aiming to accurately locate temporally ordered event sequences from natural language queries—enabling applications in e-commerce behavior analysis, social media monitoring, and criminal investigation. To advance this task, we introduce TESRBench, the first comprehensive benchmark encompassing diverse real-world, multi-source scenarios. We propose TPP-Embedding, the first unified framework integrating Large Language Models (LLMs) with Temporal Point Processes (TPPs), termed TPP-LLM. It incorporates sequence-level text-temporal joint contrastive learning, event-sequence pooling embeddings, and a hybrid text generation strategy combining synthetic data synthesis with human verification. Evaluated across the full TESRBench dataset, our method significantly outperforms existing baselines, achieving state-of-the-art performance in both retrieval accuracy and robustness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Retrieving temporal event sequences from textual descriptions is crucial for applications such as analyzing e-commerce behavior, monitoring social media activities, and tracking criminal incidents. To advance this task, we introduce TESRBench, a comprehensive benchmark for temporal event sequence retrieval (TESR) from textual descriptions. TESRBench includes diverse real-world datasets with synthesized and reviewed textual descriptions, providing a strong foundation for evaluating retrieval performance and addressing challenges in this domain. Building on this benchmark, we propose TPP-Embedding, a novel model for embedding and retrieving event sequences. The model leverages the TPP-LLM framework, integrating large language models (LLMs) with temporal point processes (TPPs) to encode both event texts and times. By pooling representations and applying a contrastive loss, it unifies temporal dynamics and event semantics in a shared embedding space, aligning sequence-level embeddings of event sequences and their descriptions. TPP-Embedding demonstrates superior performance over baseline models across TESRBench datasets, establishing it as a powerful solution for the temporal event sequence retrieval task.

Problem

• Temporal Event Extraction
• Online Shopping Habits
• Social Media Dynamics

Innovation

• TESRBench
• TPP-Embedding
• Temporal Event Processing