DC Field | Value | Language |
---|---|---|
dc.contributor.author | Woo, Sungpil | ko |
dc.contributor.author | Zubair, Muhammad | ko |
dc.contributor.author | Kim, Daeyoung | ko |
dc.date.accessioned | 2022-11-29T08:01:57Z | - |
dc.date.available | 2022-11-29T08:01:57Z | - |
dc.date.created | 2022-11-29 | - |
dc.date.created | 2022-11-29 | - |
dc.date.created | 2022-11-29 | - |
dc.date.created | 2022-11-29 | - |
dc.date.issued | 2022-10-19 | - |
dc.identifier.citation | The 13th International Conference on ICT Convergence, ICTC 2022 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | http://hdl.handle.net/10203/301263 | - |
dc.description.abstract | In this paper, we propose Cold-CFGAN, a collaborative filtering using two Conditional Generative Adversarial Networks (CGANs). In Cold-CFGAN, one CGAN is used for data augmentation of cold-start users, and the other CGAN is used to recommend items using user condition vectors. ColdCFGAN research uses an additional GAN model to generate data for cold-start users to resolve the cold start problem that occurs when implementing CGAN-based collaborative filtering and to further improve the accuracy of the model. To this end, we first identified the performance degradation problem of cold-start users through a series of preliminary experiments using an existing conditional GAN-based collaborative filtering (CFGAN). Then, we used the user profile and item purchase data to express the number of purchased items per user in the form of a percentile, and identified cold-start users with few purchase items. Using the profile of the identified cold-start user data, we found the data of the Item-Rich user with the most similar profile to the cold-start user based on the cosine similarity, and using the data of the Item-Rich user, we applied partial masking method to create augmented cold-start users. Then we train user augmentation GAN to generate fake Item-Rich user using the augmented cold-start user and corresponding ItemRich user in real data. We use trained generator to generate Item-Rich user corresponding to cold-start user in real dataset. Then, we applied the generated Item-Rich user data to train the conditional GAN-based collaborative filtering and after training, we performed experiment. Through the experiment, we found improved performance for cold start users compared to the traditional approach, and also improved overall performance. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Conditional GAN based Collaborative Filtering with Data Augmentation for Cold-Start User | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85143253988 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | The 13th International Conference on ICT Convergence, ICTC 2022 | - |
dc.identifier.conferencecountry | KO | - |
dc.identifier.conferencelocation | Ramada Plaza Hotel Jeju & Online | - |
dc.contributor.localauthor | Kim, Daeyoung | - |
dc.contributor.nonIdAuthor | Zubair, Muhammad | - |
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