Hoang-Vu Dang
ABSTRACT
Many recommender systems operate on transactional data, of which the most widely available measure is the level of interaction between users and items. In real-world applications the recommendation process is often further divided into two stages: candidate generation, which selects a number of suitable items from the whole inventory, and ranking, which determines the order of suitability among the candidates. In this paper we define two separate tests to represent the candidate generation and ranking tasks, and evaluate a diverse set of old and new collaborative filtering methods on these tests. The results on three different datasets show that model accuracy on the two tasks do not correlate strongly with each other, thus in practical applications the model choice for each task should be considered independently. Furthermore we introduce an attentional autoencoder for implicit recommendation, based on the Autorec and Transformer architectures, trained with a variational objective. The proposed model yields consistently superior results to the other evaluated methods in both the candidate generation and ranking tasks.
- O. Celma. 2010. Music Recommendation and Discovery in the Long Tail. Springer.Google Scholar
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 191--198. https: //doi.org/10.1145/2959100.2959190Google ScholarDigital Library
- Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. L, and Richard Harshman. 1990. Indexing by latent semantic analysis. Journal of the American Society of Information Science 41, 6 (1990), 391--407.Google ScholarCross Ref
- Gintare Karolina Dziugaite and Daniel M. Roy. 2015. Neural Network Matrix Factorization. CoRR abs/1511.06443 (2015). arXiv:1511.06443 http://arxiv.org/ abs/1511.06443Google Scholar
- Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, and Krishnaram Kenthapadi. 2018. Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 1353--1354.Google ScholarDigital Library
- Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Trans. Manage. Inf. Syst. 6, 4, Article 13 (Dec. 2015), 19 pages. https://doi.org/10.1145/2843948Google ScholarDigital Library
- 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, Switzerland, 173--182. https://doi.org/10.1145/3038912.3052569Google ScholarDigital Library
- Kalervo Järvelin and Jaana Kekäläinen. 2000. IR Evaluation Methods for Retrieving Highly Relevant Documents. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '00). ACM, New York, NY, USA, 41--48. https://doi.org/10.1145/345508.345545Google ScholarDigital Library
- M. G. Kendall. 1938. A New Measure of Rank Correlation. Biometrika 30, 1/2 (1938), 81--93. http://www.jstor.org/stable/2332226Google ScholarCross Ref
- Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In ICLR.Google Scholar
- Thomas N. Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. CoRR abs/1609.02907 (2016).Google Scholar
- Thomas N. Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. CoRR abs/1611.07308 (2016). arXiv:1611.07308 http://arxiv.org/abs/1611.07308Google Scholar
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (Aug. 2009), 30--37. https://doi.org/10.1109/MC.2009.263Google ScholarDigital Library
- T.K. Landauer, P.W. Foltz, and D. Laham. 1998. An introduction to latent semantic analysis. Discourse processes 25 (1998), 259--284.Google Scholar
- Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, and Samy Bengio. 2016. LLORMA: Local Low-rank Matrix Approximation. J. Mach. Learn. Res. 17, 1 (Jan. 2016), 442--465. http://dl.acm.org/citation.cfm?id=2946645.2946660Google Scholar
- Sheng Li, Jaya Kawale, and Yun Fu. 2015. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM '15). ACM, New York, NY, USA, 811--820. https://doi.org/10.1145/2806416.2806527Google ScholarDigital Library
- Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 689--698. https://doi.org/10.1145/3178876.3186150Google ScholarDigital Library
- Yuanxin Ouyang, Wenqi Liu, Wenge Rong, and Zhang Xiong. 2014. Autoencoder- Based Collaborative Filtering. In Neural Information Processing, Chu Kiong Loo, Keem Siah Yap, Kok Wai Wong, Andrew Teoh Beng Jin, and Kaizhu Huang (Eds.). Springer International Publishing, Cham, 284--291.Google Scholar
- 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 (UAI '09). AUAI Press, Arlington, Virginia, United States, 452--461. http://dl.acm.org/ citation.cfm?id=1795114.1795167Google ScholarDigital Library
- Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS'07). Curran Associates Inc., USA, 1257--1264. http://dl.acm.org/ citation.cfm?id=2981562.2981720Google ScholarDigital Library
- Ruslan Salakhutdinov and Andriy Mnih. 2008. Bayesian Probabilistic Matrix Factorization using Markov chain Monte Carlo. In Proceedings of the International Conference on Machine Learning, Vol. 25.Google ScholarDigital Library
- Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. AutoRec: Autoencoders Meet Collaborative Filtering. In Proceedings of the 24th International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA, 111--112. https://doi.org/10.1145/2740908.2742726Google ScholarDigital Library
- Sumit Sidana, Charlotte Laclau, Massih R. Amini, Gilles Vandelle, and André Bois-Crettez. 2017. KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17). ACM, New York, NY, USA, 1245--1248. https://doi.org/10.1145/3077136.3080713Google ScholarDigital Library
- Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2017. Graph Convolutional Matrix Completion. CoRR abs/1706.02263 (2017). arXiv:1706.02263 http://arxiv.org/abs/1706.02263Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 5998--6008. http://papers.nips.cc/paper/ 7181-attention-is-all-you-need.pdfGoogle ScholarDigital Library
- JizheWang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. CoRR abs/1803.02349 (2018). arXiv:1803.02349 http://arxiv.org/abs/ 1803.02349Google Scholar
- Qidi Xu, Fumin Shen, Li Liu, and Heng Tao Shen. 2018. GraphCAR: Content-aware Multimedia Recommendation with Graph Autoencoder. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18). ACM, New York, NY, USA, 981--984. https://doi.org/10.1145/3209978. 3210117Google ScholarDigital Library
- Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, and Ming-Feng Tsai. 2018. HOP-rec: High-order Proximity for Implicit Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18). ACM, New York, NY, USA, 140--144. https://doi.org/10.1145/3240323.3240381Google ScholarDigital Library
- 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 '18). ACM, New York, NY, USA, 311--319. https: //doi.org/10.1145/3240323.3240343Google ScholarDigital Library
Index Terms
- Attentional autoencoder for weighted implicit collaborative filtering
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