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Journal Article
Dialog‐based multi‐item recommendation using automatic evaluation
- Authors
- Euisok Chung, Hyun Woo Kim, Byunghyun Yoo, Ran Han, Jeongmin Yang, Hwa Jeon Song
- Issue Date
- 2024-04
- Citation
- ETRI Journal, v.46, no.2, pp.277-289
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- In this paper, we describe a neural network‐based application that recommends multiple items using dialog context input and simultaneously outputs a response sentence. Further, we describe a multi‐item recommendation by specifying it as a set of clothing recommendations. For this, a multimodal fusion approach that can process both cloth‐related text and images is required. We also examine achieving the requirements of downstream models using a pretrained language model. Moreover, we propose a gate‐based multimodal fusion and multiprompt learning based on a pretrained language model. Specifically, we propose an automatic evaluation technique to solve the one‐to‐many mapping problem of multi‐item recommendations. A fashion‐domain multimodal dataset based on Koreans is constructed and tested. Various experimental environment settings are verified using an automatic evaluation method. The results show that our proposed method can be used to obtain confidence scores for multi‐item recommendation results, which is different from traditional accuracy evaluation.
- KSP Keywords
- Accuracy evaluation, Evaluation method, Evaluation technique, Fusion approach, Item recommendation, Language Model, Multimodal dataset, Text and images, automatic evaluation, multimodal fusion, neural network(NN)
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
