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Generative Adversarial Framework for Cold-Start Item Recommendation

Published: 07 July 2022 Publication History

Abstract

The cold-start problem has been a long-standing issue in recommendation. Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions. Therefore, such embedding-based models perform badly for cold items which haven't emerged in the training set. The most common solutions are to generate the cold embedding for the cold item from its content features. However, the cold embeddings generated from contents have different distribution as the warm embeddings are learned from historical interactions. In this case, current cold-start methods are facing an interesting seesaw phenomenon, which improves the recommendation of either the cold items or the warm items but hurts the opposite ones. To this end, we propose a general framework named Generative Adversarial Recommendation (GAR). By training the generator and the recommender adversarially, the generated cold item embeddings can have similar distribution as the warm embeddings that can even fool the recommender. Simultaneously, the recommender is fine-tuned to correctly rank the "fake'' warm embeddings and the real warm embeddings. Consequently, the recommendation of the warms and the colds will not influence each other, thus avoiding the seesaw phenomenon. Additionally, GAR could be applied to any off-the-shelf recommendation model. Experiments on two datasets present that GAR has strong overall recommendation performance in cold-starting both the CF-based model (improved by over 30.18%) and the GNN-based model (improved by over 17.78%).

References

[1]
Fabian Abel, Yashar Deldjoo, Mehdi Elahi, and Daniel Kohlsdorf. 2017. RecSys Challenge 2017: Offline and Online Evaluation. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 372--373.
[2]
Martín Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6--11 August 2017 (Proceedings of Machine Learning Research, Vol. 70). PMLR, 214--223.
[3]
Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jaeho Choi. 2019. Rating Augmentation with Generative Adversarial Networks towards Accurate Collaborative Filtering. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13--17, 2019. ACM, 2616--2622.
[4]
Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018. CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22--26, 2018. ACM, 137--146.
[5]
Hao Chen, Zengde Deng, Yue Xu, and Zhoujun Li. 2021. Non-Recursive Graph Convolutional Networks. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, Toronto, ON, Canada, June 6--11, 2021. IEEE, 3205--3209.
[6]
Hao Chen, Wenbing Huang, Yue Xu, Fuchun Sun, and Zhoujun Li. 2020. Graph Unfolding Networks. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19--23, 2020. ACM, 1981--1984.
[7]
Hao Chen, Zhong Huang, Yue Xu, Zengde Deng, Feiran Huang, Peng He, and Zhoujun Li. 2022. Neighbor enhanced graph convolutional networks for node classification and recommendation. Knowledge-Based Systems (2022), 108594.
[8]
Hao Chen, Yue Xu, Feiran Huang, Zengde Deng, Wenbing Huang, Senzhang Wang, Peng He, and Zhoujun Li. 2020. Label-Aware Graph Convolutional Networks. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19--23, 2020. ACM, 1977-- 1980.
[9]
Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, and Hongbo Deng. 2020. ESAM: Discriminative Domain Adaptation with NonDisplayed Items to Improve Long-Tail Performance. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020. ACM, 579--588.
[10]
Yunjey Choi, Min-Je Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. 2018. StarGAN: Unified Generative Adversarial Networks for MultiDomain Image-to-Image Translation. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18--22, 2018. Computer Vision Foundation / IEEE Computer Society, 8789--8797.
[11]
Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15--19, 2016. ACM, 191--198.
[12]
Manqing Dong, Feng Yuan, Lina Yao, Xiwei Xu, and Liming Zhu. 2020. MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020. ACM, 688--697.
[13]
Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable Representation Learning for Heterogeneous Networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017. ACM, 135--144.
[14]
Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, and Tat-Seng Chua. 2020. How to Learn Item Representation for Cold-Start Multimedia Recommendation?. In MM '20: The 28th ACM International Conference on Multimedia, Virtual Event / Seattle, WA, USA, October 12--16, 2020. ACM, 3469--3477.
[15]
Shaohua Fan, Junxiong Zhu, Xiaotian Han, Chuan Shi, Linmei Hu, Biyu Ma, and Yongliang Li. 2019. Metapath-Guided Heterogeneous Graph Neural Network for Intent Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19). Association for Computing Machinery, New York, NY, USA, 2478--2486.
[16]
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic MetaLearning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6--11 August 2017 (Proceedings of Machine Learning Research, Vol. 70). PMLR, 1126--1135. http://proceedings.mlr.press/v70/finn17a.html
[17]
Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, and Jin Li. 2019. Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 94--101.
[18]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems, Vol. 27. Curran Associates, Inc., Montreal, Quebec, Canada, 2672--2680.
[19]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13--17, 2016. ACM, 855--864.
[20]
Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, and Aaron C. Courville. 2017. Improved Training of Wasserstein GANs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA. 5767--5777.
[21]
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19--25, 2017. ijcai.org, 1725-- 1731.
[22]
James Hale. 2019. More than 500 hours of content are now being uploaded to YouTube every minute. https://www.tubefilter.com/2019/05/07/number-hoursvideo-uploaded-to-youtube-per-minute/
[23]
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA. 1024--1034.
[24]
Ming He, Han Wen, and Hanyu Zhang. 2021. LGCCF: A Linear Graph Convolutional Collaborative Filtering with Social Influence. In Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11--14, 2021, Proceedings, Part III (Lecture Notes in Computer Science, Vol. 12683). Springer, 306--314.
[25]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 639--648.
[26]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173-- 182.
[27]
Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative Metric Learning. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 193--201.
[28]
Feiran Huang, Alireza Jolfaei, and Ali Kashif Bashir. 2021. Robust Multimodal Representation Learning With Evolutionary Adversarial Attention Networks. IEEE Trans. Evol. Comput. 25, 5 (2021), 856--868.
[29]
Feiran Huang, Xiaoming Zhang, and Zhoujun Li. 2018. Learning Joint Multimodal Representation with Adversarial Attention Networks. In 2018 ACM Multimedia Conference on Multimedia Conference, MM 2018, Seoul, Republic of Korea, October 22--26, 2018. ACM, 1874--1882.
[30]
Wen-bing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. 2018. Adaptive Sampling Towards Fast Graph Representation Learning. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montréal, Canada. 4563--4572.
[31]
Alexia Jolicoeur-Martineau. 2019. The relativistic discriminator: a key element missing from standard GAN. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net.
[32]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net.
[33]
Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (2009), 30--37.
[34]
Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4--8, 2019. ACM, 1073--1082.
[35]
Jin Li, Zhaohui Peng, Senzhang Wang, Xiaokang Xu, Philip S. Yu, and Zhenyun Hao. 2020. Heterogeneous Graph Embedding for Cross-Domain Recommendation Through Adversarial Learning. In Database Systems for Advanced Applications (Cham). Springer International Publishing, 507--522.
[36]
Hank Liao, Erik McDermott, and Andrew Senior. 2013. Large scale deep neural network acoustic modeling with semi-supervised training data for YouTube video transcription. In 2013 IEEE Workshop on Automatic Speech Recognition and Understanding. 368--373.
[37]
Yun Liu, Xiaoming Zhang, Feiran Huang, Lei Cheng, and Zhoujun Li. 2021. Adversarial Learning With Multi-Modal Attention for Visual Question Answering. IEEE Trans. Neural Networks Learn. Syst. 32, 9 (2021), 3894--3908.
[38]
Yuanfu Lu, Yuan Fang, and Chuan Shi. 2020. Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020. ACM, 1563--1573.
[39]
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research 9, 86 (2008), 2579--2605.
[40]
Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, Zhen Wang, and Stephen Paul Smolley. 2017. Least Squares Generative Adversarial Networks. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22--29, 2017. IEEE Computer Society, 2813--2821.
[41]
Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5--8, 2013, Lake Tahoe, Nevada, United States. 3111--3119.
[42]
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral Normalization for Generative Adversarial Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.
[43]
Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, and Qing He. 2019. Warm Up Cold-Start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 695--704.
[44]
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: online learning of social representations. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014. ACM, 701--710.
[45]
Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4--8, 2019. ACM, 2671--2679.
[46]
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020. Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19--23, 2020. ACM, 2685--2692.
[47]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, Montreal, QC, Canada, 452--461.
[48]
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net.
[49]
Paolo Rosso, Dingqi Yang, and Philippe Cudré-Mauroux. 2020. Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20--24, 2020. ACM / IW3C2, 1885--1896.
[50]
Shaoyun Shi, Min Zhang, Xinxing Yu, Yongfeng Zhang, Bin Hao, Yiqun Liu, and Shaoping Ma. 2019. Adaptive Feature Sampling for Recommendation with Missing Content Feature Values. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3--7, 2019. ACM, 1451--1460.
[51]
Changfeng Sun, Han Liu, Meng Liu, Zhaochun Ren, Tian Gan, and Liqiang Nie. 2020. LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation. In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3--7, 2020. ACM, 582--590.
[52]
John C. Tang, Gina Venolia, and Kori M. Inkpen. 2016. Meerkat and Periscope: I Stream, You Stream, Apps Stream for Live Streams. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, NY, USA, 4770--4780.
[53]
Xiaoya Tang, Tieyun Qian, and Zhenni You. 2020. Generating behavior features for cold-start spam review detection with adversarial learning. Inf. Sci. 526 (2020), 274--288.
[54]
Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2017. Graph Convolutional Matrix Completion. (2017). arXiv:1706.02263 http://arxiv.org/abs/1706. 02263
[55]
Aaron van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In Advances in Neural Information Processing Systems, Vol. 26. Curran Associates, Inc., Lake Tahoe, Nevada, United States, 2643--2651.
[56]
Maksims Volkovs, Guang Wei Yu, and Tomi Poutanen. 2017. DropoutNet: Addressing Cold Start in Recommender Systems. In Advances in Neural Information Processing Systems, Vol. 30. Long Beach, CA, USA, 4957--4966.
[57]
Chong Wang and David M. Blei. 2011. Collaborative Topic Modeling for Recommending Scientific Articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11). Association for Computing Machinery, New York, NY, USA, 448--456.
[58]
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. GraphGAN: Graph Representation Learning With Generative Adversarial Nets. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, Louisiana, USA, February 2--7, 2018. AAAI Press, 2508--2515.
[59]
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7--11, 2017. ACM, 515--524.
[60]
Senzhang Wang, Hao Miao, Hao Chen, and Zhiqiu Huang. 2020. Multi-Task Adversarial Spatial-Temporal Networks for Crowd Flow Prediction. In Proceedings of the 29th ACM International Conference on Information amp; Knowledge Management (Virtual Event, Ireland) (CIKM '20). Association for Computing Machinery, New York, NY, USA, 1555--1564.
[61]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 165--174.
[62]
Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan Li, Xuanping Li, and Tat-Seng Chua. 2021. Contrastive Learning for Cold-Start Recommendation. In MM '21: ACM Multimedia Conference, Virtual Event, China, October 20 - 24, 2021. ACM, 5382--5390.
[63]
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed H. Chi. 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16--20, 2019. ACM, 269--277.
[64]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). Association for Computing Machinery, New York, NY, USA, 974--983.
[65]
Muhan Zhang and Yixin Chen. 2018. Link Prediction Based on Graph Neural Networks. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montréal, Canada. 5171--5181.
[66]
Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, and Xiaofei He. 2019. IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19). Association for Computing Machinery, New York, NY, USA, 2347--2357.
[67]
Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, and Philip S. Yu. 2018. Spectral collaborative filtering. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2--7, 2018. ACM, 311--319.
[68]
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep Interest Evolution Network for Click-Through Rate Prediction. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019. AAAI Press, 5941--5948.
[69]
Guorui Zhou, Xiaoqiang Zhu, Chengru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep Interest Network for Click-Through Rate Prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2018. ACM, 1059--1068.
[70]
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22--29, 2017. IEEE Computer Society, 2242--2251.
[71]
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang, Kaikai Ge, Ying Sun, Xu Zhang, Leyu Lin, and Juan Cao. 2021. Learning to Warm Up Cold Item Embeddings for Cold-Start Recommendation with Meta Scaling and Shifting Networks. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 1167--1176.
[72]
Ziwei Zhu, Shahin Sefati, Parsa Saadatpanah, and James Caverlee. 2020. Recommendation for New Users and New Items via Randomized Training and Mixtureof-Experts Transformation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 1121--1130.
[73]
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, and Jian Tang. 2021. Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction. (2021). arXiv:2106.06935 https://arxiv.org/abs/2106.06935

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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
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  2. cold-start recommendation
  3. recommender system

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