Developing a Recommendation Benchmark for MLPerf Training and Inference

Scalable realistic recommendation datasets through fractal expansions

Original

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The architectural implications of Facebook’s DNN-based personalized recommendation

Original

Gupta, U., Wu, C.-J., Wang, X., Naumov, M., Reagen, B., Brooks, D., Cottel, B., Hazelwood, K. M., Jia, B., Lee, H. S., Malevich, A., Mudigere, D., Smelyanskiy, M., Xiong, L., and Zhang, X · 1906

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Deep learning recommendation model for personalization and recommendation systems

Original

Naumov, M., Mudigere, D., Shi, H. M., Huang, J., Sundaraman, N., Park, J., Wang, X., Gupta, U., Wu, C., Azzolini, A. G., Dzhulgakov, D., Mallevich, A., Cherniavskii, I., Lu, Y., Krishnamoorthi, R., Yu, A., Kondratenko, V., Pereira, S., Chen, X., Chen, W., Rao, V., Jia, B., Xiong, L., and Smelyanskiy, M · 1906

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Matrix factorization techniques for recommender systems

Koren, Y., Bell, R., and Volinsky, C · 2009

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Factorization machines

Rendle, S · 2010

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The movielens datasets: History and context

Harper, F. M. and Konstan, J. A · 2015

Cited alongside, same era.

Wide & deep learning for recommender systems

Original

Cheng, H., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., Anil, R., Haque, Z., Hong, L., Jain, V., Liu, X., and Shah, H · 2016

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In-datacenter performance analysis of a tensor processing unit

Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., and et al · 2017

Cited alongside, same era.

Deep & cross network for ad click predictions

Original

Wang, R., Fu, B., Fu, G., and Wang, M · 2017

Cited alongside, same era.

Deep interest network for click-through rate prediction

Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., and Gai, K · 2018

Cited alongside, same era.

Mlperf training benchmark

Original

Mattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., Tang, H., Wei, G.-Y., Bailis, P., Bittorf, V., Brooks, D., Chen, D., Dutta, D., Gupta, U., Hazelwood, K., Hock, A., Huang, X., Jia, B., Kang, D., Kanter, D., Kumar, N., Liao, J., Narayanan, D., Oguntebi, T., Pekhimenko, G., Pentecost, L., Reddi, V. J., Robie, T., John, T. S., Wu, C.-J., Xu, L., Young, C., and Zaharia, M · 2019

Later among the works it cites.

Mlperf inference benchmark

Original

Reddi, V. J., Cheng, C., Kanter, D., Mattson, P., Schmuelling, G., Wu, C.-J., Anderson, B., Breughe, M., Charlebois, M., Chou, W., Chukka, R., Coleman, C., Davis, S., Deng, P., Diamos, G., Duke, J., Fick, D., Gardner, J. S., Hubara, I., Idgunji, S., Jablin, T. B., Jiao, J., John, T. S., Kanwar, P., Lee, D., Liao, J., Lokhmotov, A., Massa, F., Meng, P., Micikevicius, P., Osborne, C., Pekhimenko, G., Rajan, A. T. R., Sequeira, D., Sirasao, A., Sun, F., Tang, H., Thomson, M., Wei, F., Wu, E., Xu, L., Yamada, K., Yu, B., Yuan, G., Zhong, A., Zhang, P., and Zhou, Y · 2019

Later among the works it cites.

Recommending what video to watch next: A multitask ranking system

Zhao, Z., Hong, L., Wei, L., Chen, J., Nath, A., Andrews, S., Kumthekar, A., Sathiamoorthy, M., Yi, X., and Chi, E · 2019

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Deep interest evolution network for click-through rate prediction