TemPEST: Soft Template-Based Personalized EDM Subject Generation through Collaborative Summarization
Authors: Yu-Hsiu Chen, Pin-Yu Chen, Hong-Han Shuai, Wen-Chih Peng7538-7545
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that Tem PEST is able to generate personalized topics and also effectively perform recommending rating reconstruction. |
| Researcher Affiliation | Academia | Yu-Hsiu Chen, Pin-Yu Chen, Hong-Han Shuai, Wen-Chih Peng National Chiao Tung University, Hsinchu, Taiwan {yhchen.cm06g, pinyu.eed04, hhshuai}@nctu.edu.tw, wcpeng@g2.nctu.edu.tw |
| Pseudocode | No | The paper describes the proposed model architecture and processes in prose and with diagrams (Figure 1, Figure 2) but does not include any explicitly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | Yes | More details of dataset and case studies are shown in the supplementary material.5 https://github.com/yhchen2/TemPEST |
| Open Datasets | Yes | Since there is no public dataset for personalized subject summarization, we collect a new one named KKday from the well-known travel experience e-commerce platform in Asia that sales tour packages.4 The raw data contains the paired tuple (article, subject) of tour package DMs, together with the ID list of users who click the subject. [...] More details of dataset and case studies are shown in the supplementary material.5 https://github.com/yhchen2/TemPEST |
| Dataset Splits | Yes | For summarization task, we randomly split the dataset into 14095 products for training, 1761 for testing and 1761 for validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'The Word2Vec is implemented with gensim and pretrained on the latest Chinese Wikipedia' and 'We use two-layer Bi LSTM for both TSE and USE network'. While 'gensim' is a library, no version number is provided for it or any other specific software component used in their implementation. |
| Experiment Setup | Yes | We use two-layer Bi LSTM for both TSE and USE network, and the hidden state size of 500. The learning rate and dropout rate are set to 0.001 and 0.3 respectively. |