Retrieval of Temporal Event Sequences from Textual Descriptions (2025)

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.

## introduction temporal event sequence retrieval @xcite plays a crucial role in various applications, such as e-commerce user activity analysis, social media monitoring, and crime tracking. these sequences combine temporal information with event types, making them more complex than traditional text data. effective retrieval requires models capable of capturing both time-sensitive dynamics and structured relationships within the sequences. while traditional language models perform well for general text retrieval @xcite , they * these authors contributed equally to this work. tpp-llm + pooling tpp-llm + pooling sequence description event sequence similarity description embedding sequence embedding the sequence of taxi trips primarily consists of pickups and dropoffs occurring in manhattan, ... 0.00, manhattan pickup 0.19, manhattan dropoff 0.24, manhattan pickup ... often struggle to handle the unique temporal and structural complexities of event sequences. to address these challenges, we introduce tesr-bench foot_0 , a comprehensive benchmark for evaluating temporal event sequence retrieval (tesr) from textual descriptions. tesrbench comprises diverse real-world event sequence datasets with synthesized and reviewed textual descriptions, offering a strong foundation for benchmarking retrieval models. it highlights the complexities of aligning event sequences with textual descriptions and provides a standardized platform for evaluating model performance, uncovering key challenges, and identifying opportunities for improvement in temporal and contextual modeling. building on this benchmark, we propose tpp-embedding 2 , a novel framework for temporal event sequence retrieval that extends the tpp-llm model @xcite . tpp-llm integrates temporal encoding for event times and textual embeddings for event types within a large language model (llm) backbone to model temporal point processes @xcite . extending this framework, tpp-embedding aligns sequence-level representations of event sequences and their textual descriptions in a shared embedding space. by modeling the interdependencies between events and their temporal context, tpp-embedding generates richer, contextually informed embeddings optimized for retrieval tasks. evaluated across tesrbench datasets, tpp-embedding demonstrates superior performance over text-based baselines and generalizes effectively across different event domains. in this paper, our key contributions are: (1) introducing tesrbench, a benchmark for evaluating tesr models with diverse datasets; (2) proposing tpp-embedding, which integrates temporal and event-type information for accurate event sequence retrieval from descriptions; and (3) showcasing the scalability and flexibility of our approach through multi-domain experiments. ## related work recent developments in sentence representation models, such as sentence-bert @xcite , have significantly improved retrieval tasks by enabling efficient semantic similarity searches using transformer-based embeddings @xcite . while these models perform well in standard text retrieval tasks @xcite , they struggle with the temporal and event-specific complexities of event sequence data. to address these challenges, temporal point process (tpp) models @xcite @xcite have been adapted for retrieval tasks. neuroseqret @xcite introduces a neural framework for continuous-time event sequence retrieval by leveraging marked tpps to model temporal dynamics and using a trainable unwarping function, neural relevance models, and hashing techniques to optimize retrieval efficiency. however, despite these advancements, existing models either treat event types as categorical inputs, limiting their ability to capture rich event semantics, or treat entire sequences as text, ignoring their temporal dependencies. recently, @xcite proposed tpp-llm, a framework that integrates large language models (llms) with tpps to capture event semantics and temporal dynamics for event sequence modeling and prediction. while tpp-llm focuses on predicting future event types and times using both textual and temporal information, our proposed tpp-embedding extends this framework to the task of retrieving temporal event sequences from textual descriptions. by introducing a shared embedding space for sequences and descriptions and employing contrastive learning, our model effectively aligns sequence-level representations with natural language descriptions, enabling retrieval while maintaining temporal and semantic dependencies. ## benchmark in this section, we present tesrbench, a comprehensive benchmark designed to evaluate temporal event sequence retrieval (tesr) from textual descriptions. we provide an overview of its key components, including detailed dataset summaries, the methodology for generating event sequence descriptions, and the evaluation process used to assess the quality of these descriptions. ## methodology in this section, we introduce tpp-embedding, an extension of tpp-llm (liu and quan, 2024), de-dataset acc. cov. fid. cla. con. ## experiments in this section, we present a detailed overview of the baseline models used for comparison, the evaluation metrics employed, the experimental setup, the results obtained, and the ablation studies conducted. ## conclusion in this paper, we introduce tesrbench, a comprehensive benchmark for evaluating temporal event sequence retrieval, alongside tpp-embedding, a novel model designed to integrate temporal and event-type-aware representations. tesrbench provides a diverse set of datasets with synthesized textual descriptions, offering a robust foundation for benchmarking models in this domain. our proposed tpp-embedding model combines temporal encoding and event text embedding with a large language model backbone, enabling it to effectively capture the structure and dependencies of temporal event sequences. extensive experiments conducted on tesrbench demonstrate its superior performance compared to traditional text-based baselines, particularly in handling temporally complex, multitype event sequences. furthermore, multi-domain experiments underscore the flexibility and adaptability of our approach across diverse event domains. together, tesrbench and tpp-embedding represent a significant step forward in advancing research on temporal event sequence retrieval.