Temporal Event Sequences with Descriptions
Collection
A collection of real-world temporal event sequences from multiple domains with sequence descriptions generated by GPT-4o. • 7 items • Updated • 1
dim_process int64 18 18 | seq_idx int64 0 1.8k | seq_len int64 40 182 | time_since_start sequencelengths 40 182 | time_since_last_event sequencelengths 40 182 | type_event sequencelengths 40 182 | type_text sequencelengths 40 182 | description stringlengths 428 999 |
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"Kindle S... | The sequence of product reviews begins with a consistent focus on the "Books" category, with multiple reviews submitted in the initial weeks. After a sustained period of reviewing books, there is a shift towards the "Kindle Store" category, which starts to appear alongside the book reviews around the 10-week mark. The ... |
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... | The sequence of product reviews begins with a few entries in the Kindle Store, followed by a significant cluster of reviews for Books that dominate the timeline. After the initial reviews, there is a consistent pattern of Book reviews occurring at regular intervals, with only occasional returns to the Kindle Store. As ... |
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... | The sequence of product reviews begins with a few entries in the Kindle Store, followed by a significant cluster of reviews focused on Books. The majority of the reviews are concentrated in the Books category, which continues to dominate throughout the timeline, with a few intermittent entries in the Kindle Store. Ther... |
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"Books... | The sequence of product reviews begins with a focus on the Kindle Store and Books, with initial reviews occurring simultaneously. Over time, there is a notable predominance of reviews for Books, particularly in the early weeks, with Kindle Store reviews appearing less frequently. As the sequence progresses, there are c... |
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... | The sequence of product reviews shows a pattern where the user primarily reviews books and Kindle Store products. In the initial weeks, reviews for both categories appear closely together, with a slight emphasis on books. As time progresses, there is a noticeable increase in Kindle Store reviews, particularly around th... |
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... | The sequence of product reviews shows a predominant focus on the "Books" category, with reviews consistently submitted over an extended period. The "Kindle Store" reviews appear intermittently, primarily clustered around the early and later weeks of the sequence. There is a noticeable trend of increased activity in the... |
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"Industrial ... | The sequence of product reviews shows a consistent pattern of submissions primarily focused on Home and Kitchen products in the early weeks, with a notable concentration of reviews in this category during the first month. As time progresses, there is a diversification into other categories such as Patio Lawn and Garden... |
18 | 52 | 60 | [
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18 | 53 | 48 | [
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"Bo... | The sequence of product reviews shows a strong emphasis on the "Books" category, with the majority of reviews submitted consistently over time. The first review occurs within the first week, and reviews in this category continue to dominate throughout the sequence, with only a few instances of other categories such as ... |
18 | 54 | 48 | [
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"Books",... | The sequence of product reviews consists exclusively of reviews in the "Books" category, with a consistent pattern of submissions over time. The reviews are clustered closely together, especially in the earlier weeks, with a notable frequency of submissions occurring in the first 12 weeks. As time progresses, the frequ... |
18 | 55 | 50 | [
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18 | 56 | 62 | [
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18 | 57 | 51 | [
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18 | 58 | 50 | [
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"Book... | The sequence of product reviews predominantly features the "Books" category, with a consistent frequency of reviews submitted over time. Most reviews occur within the first 24 weeks, with a notable cluster of reviews concentrated in the initial weeks. The first instance of reviews for the "Kindle Store" appears around ... |
End of preview. Expand in Data Studio
Amazon Review Description Dataset
This dataset contains Amazon reviews from January 1, 2018, to June 30, 2018. It includes 2,245 sequences with 127,054 events across 18 category types. The original data is available at Amazon Review Data with citation information provided on the page. The detailed data preprocessing steps used to create this dataset can be found in the TPP-LLM paper and TPP-Embedding paper.
If you find this dataset useful, we kindly invite you to cite the following papers:
@article{liu2024tppllmm,
title={TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models},
author={Liu, Zefang and Quan, Yinzhu},
journal={arXiv preprint arXiv:2410.02062},
year={2024}
}
@article{liu2024retrieval,
title={Retrieval of Temporal Event Sequences from Textual Descriptions},
author={Liu, Zefang and Quan, Yinzhu},
journal={arXiv preprint arXiv:2410.14043},
year={2024}
}
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