ABSTRACT
Generative Adversarial Networks (GAN) have achieved big success in various domains such as image generation, music generation, and natural language generation. In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation. We first identify a fundamental problem of existing GAN-based methods in CF and highlight it quantitatively via a series of experiments. Next, we suggest a new direction of vector-wise adversarial training to solve the problem and propose our GAN-based CF framework, called CFGAN, based on the direction. We identify a unique challenge that arises when vector-wise adversarial training is employed in CF. We then propose three CF methods realized on top of our CFGAN that are able to address the challenge. Finally, via extensive experiments on real-world datasets, we validate that vector-wise adversarial training employed in CFGAN is really effective to solve the problem of existing GAN-based CF methods. Furthermore, we demonstrate that our proposed CF methods on CFGAN provide recommendation accuracy consistently and universally higher than those of the state-of-the-art recommenders.
- Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17, 6 (2005), 734--749. Google ScholarDigital Library
- Antreas Antoniou, Amos Storkey, and Harrison Edwards. 2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 (2017).Google Scholar
- Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017).Google Scholar
- David Berthelot, Thomas Schumm, and Luke Metz. 2017. BEGAN: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717 (2017).Google Scholar
- Edward Choi et al. 2017. Generating multi-label discrete electronic health records using generative adversarial networks. arXiv preprint arXiv:1703.06490 (2017).Google Scholar
- Yunjey Choi et al. 2017. StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. arXiv preprint arXiv:1711.09020 (2017).Google Scholar
- Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In ACM Recsys. 39--46. Google ScholarDigital Library
- Chris Donahue, Julian McAuley, and Miller Puckette. 2018. Synthesizing Audio with Generative Adversarial Networks. arXiv preprint arXiv:1802.04208 (2018).Google Scholar
- Maayan Frid-Adar et al. 2018. GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification. arXiv preprint arXiv:1803.01229 (2018).Google Scholar
- Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. 249--256.Google Scholar
- Ian Goodfellow et al. 2014. Generative adversarial nets. In NIPS. 2672--2680. Google ScholarDigital Library
- Ishaan Gulrajani et al. 2017. Improved training of wasserstein gans. In NIPS. 5769--5779.Google Scholar
- Xiangnan He et al. 2017. Neural collaborative filtering. In ACM WWW. 173--182. Google ScholarDigital Library
- Geoffrey E Hinton and Ruslan R Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313, 5786 (2006), 504--507.Google Scholar
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In IEEE ICDM. 263--272. Google ScholarDigital Library
- Cong Phuoc Huynh et al. 2018. CRAFT: Complementary Recommendations Using Adversarial Feature Transformer. arXiv preprint arXiv:1804.10871 (2018).Google Scholar
- Won-Seok Hwang et al. 2016. "Told you i didn't like it": Exploiting uninteresting items for effective collaborative filtering. In IEEE ICDE. 349--360.Google Scholar
- Santosh Kabbur, Xia Ning, and George Karypis. 2013. Fism: factored item similarity models for top-n recommender systems. In ACM SIGKDD. 659--667. Google ScholarDigital Library
- Wang-Cheng Kang et al. 2017. Visually-Aware Fashion Recommendation and Design with Generative Image Models. arXiv preprint arXiv:1711.02231 (2017).Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In ACM SIGKDD. 426--434. Google ScholarDigital Library
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009). Google ScholarDigital Library
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444.Google Scholar
- Hung-yi Lee and Yu Tsao. 2018. Generative Adversarial Network and its Applications to Speech Signal and Natural Language Processing. (2018).Google Scholar
- Yeon-Chang Lee, Sang-Wook Kim, and Dongwon Lee. 2018. gOCCF: Graphtheoretic one-class collaborative filtering based on uninteresting items. In AAAI. 3448--3456.Google Scholar
- Jongwuk Lee et al. 2016. Improving the accuracy of top-N recommendation using a preference model. Information Sciences 348 (2016), 290--304. Google ScholarDigital Library
- Youngnam Lee et al. 2018. How to impute missing ratings?: Claims, solution, and its application to collaborative filtering. In ACM WWW. 783--792. Google ScholarDigital Library
- Xudong Mao et al. 2017. Least squares generative adversarial networks. In IEEE ICCV. 2813--2821.Google Scholar
- Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).Google Scholar
- Andriy Mnih and Ruslan R Salakhutdinov. 2008. Probabilistic matrix factorization. In NIPS. 1257--1264. Google ScholarDigital Library
- Xia Ning and George Karypis. 2011. Slim: Sparse linear methods for top-n recommender systems. In IEEE ICDM. 497--506. Google ScholarDigital Library
- Rong Pan et al. 2008. One-class collaborative filtering. In IEEE ICDM. 502--511. Google ScholarDigital Library
- Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015).Google Scholar
- Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In ACM WSDM. 273--282. Google ScholarDigital Library
- Steffen Rendle et al. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452--461. Google ScholarDigital Library
- Suvash Sedhain et al. 2015. Autorec: Autoencoders meet collaborative filtering. In ACM WWW. 111--112. Google ScholarDigital Library
- Yue Shi et al. 2012. CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In ACM RecSys. 139--146. Google ScholarDigital Library
- Yong-Siang Shih et al. 2017. Compatibility family learning for item recommendation and generation. arXiv preprint arXiv:1712.01262 (2017).Google Scholar
- Sumit Sidana et al. 2017. Representation learning and pairwise ranking for implicit feedback in recommendation systems. arXiv preprint arXiv:1705.00105 (2017).Google Scholar
- Jiliang Tang, Huiji Gao, and Huan Liu. 2012. mTrust: Discerning multi-faceted trust in a connected world. In ACM WSDM. 93--102. Google ScholarDigital Library
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In ACM SIGKDD. 1235--1244. Google ScholarDigital Library
- Hongwei Wang et al. 2018. GraphGAN: Graph representation learning with generative adversarial nets. In AAAI. 2508--2515.Google Scholar
- Jun Wang et al. 2017. IRGAN: A minimax game for unifying generative and discriminative information retrieval models. In ACM SIGIR. 515--524. Google ScholarDigital Library
- Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. In Reinforcement Learning. Springer, 5--32.Google Scholar
- Yao Wu et al. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In ACM WSDM. 153--162. Google ScholarDigital Library
- Lantao Yu et al. 2017. SeqGAN: Sequence generative adversarial nets with policy gradient.. In AAAI. 2852--2858.Google Scholar
- Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou. 2016. A neural autoregressive approach to collaborative filtering. In ICML. 764--773. Google ScholarDigital Library
Index Terms
- CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks
Recommendations
Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering
WWW '19: The World Wide Web ConferenceGenerative Adversarial Networks (GAN) have not only achieved a big success in various generation tasks such as images, but also boosted the accuracy of classification tasks by generating additional labeled data, which is called data augmentation. In ...
CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users
WWW '19: The World Wide Web ConferenceA major drawback of cross-network recommender solutions is that they can only be applied to users that are overlapped across networks. Thus, the non-overlapped users, which form the majority of users are ignored. As a solution, we propose CnGAN, a novel ...
Convex AUC optimization for top-N recommendation with implicit feedback
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systemsIn this paper, an effective collaborative filtering algorithm for top-N item recommendation with implicit feedback is proposed. The task of top-N item recommendation is to predict a ranking of items (movies, books, songs, or products in general) that ...
Comments