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학술대회 Conditional GAN based Collaborative Filtering with Data Augmentation for Cold-Start User
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저자
우성필, 주바이르, 임선환, 박찬원, 김대영
발행일
202210
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1756-1761
DOI
https://dx.doi.org/10.1109/ICTC55196.2022.9952471
협약과제
22HR4200, 자율형 IoT 디바이스 자율협업 집단지능 프레임워크 기술 개발, 임선환
초록
In this paper, we propose Cold-CFGAN, a collab-orative 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. Cold-CFGAN 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 Item-Rich 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.
KSP 제안 키워드
Cold-start Problem, Cold-start users, Collaborative filtering(CF), Cosine similarity, Data Augmentation, GaN-Based, Masking method, Overall performance, Real data, Similar profile, Traditional approach