Papers by Indu Indu
Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)
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Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Zou, Peng Dai, Roberto Galan, Michael Porter, Dongmei Jia, Ning Zhang, Lian Xiong
| Challenge: | Existing fashion recommendation systems struggle with the unique challenges of the fashion domain. |
| Approach: | They propose a sequential fashion recommendation framework that leverages a pre-trained large language model enhanced with recommendation-specific prompts. |
| Outcome: | The proposed framework significantly improves fashion recommendation performance on Amazon fashion. |
Word Embedding Evaluation for Sinhala (2020.lrec-1)
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| Challenge: | Word embeddings are a key component of the natural language processing process. |
| Approach: | They evaluate three standard word embedding models for Sinhala language using two evaluation methods: intrinsic evaluation and extrinsic evaluation. |
| Outcome: | The proposed models performed best in the three evaluation tasks, while FastText and Glove showed the lowest accuracies. |