Derek Zhiyuan Cheng
Derek is a principal software engineer and engineering director at Google DeepMind, where he leads a team of ML researchers and engineers tackling problem-driven research and product integration. Recent interests include 1. efficient data representation learning; 2. model and data efficiency for RecSys & LLMs; and 3. user and ads modeling with LLMs. Derek has 40+ research publications in the field, and has contributed to over 100 launches in Google Ads, Play, Search and YouTube. Prior to joining Google in 2013, he finished his Ph.D. in Texas A&M University with a focus on geo-social data mining. Derek is also a proud Pinterest alumnus that really enjoyed building Pinterest's Homefeed with the homies.
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Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Ben Coleman
Ruoxi Wang
Lichan Hong
Advances in Neural Information Processing Systems (2023)
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Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems. A typical model ingests hundreds of features with vocabularies on the order of millions to billions of tokens. The standard approach is to represent each feature value as a d-dimensional embedding, which introduces hundreds of billions of parameters for extremely high-cardinality features. This bottleneck has led to substantial progress in alternative embedding algorithms. Many of these methods, however, make the assumption that each feature uses an independent embedding table. This work introduces a simple yet highly effective framework, Feature Multiplexing, where one single representation space is used for many different categorical features. Our theoretical and empirical analysis reveals that multiplexed embeddings can be decomposed into components from each constituent feature, allowing models to distinguish between features. We show that multiplexed representations give Pareto-optimal space-accuracy tradeoffs for three public benchmark datasets. Further, we propose a highly practical approach called Unified Embedding with three major benefits: simplified feature configuration, strong adaptation to dynamic data distributions, and compatibility with modern hardware. Unified embedding gives significant improvements in offline and online metrics compared to highly competitive baselines across five web-scale search, ads, and recommender systems, where it serves billions of users across the world in industry-leading products.
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Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Dong Lin
Lichan Hong
Yuyan Wang
WSDM (2021) (to appear)
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In recent years, various deep neural network (DNN) models led to stellar performance in various domains. However, ML practitioners and researchers have observed severe reproducibility issues on DNN models. That is, a set of DNN models trained on the same data with exactly the same architecture may lead to quite different predictions. A common remedy is to use the ensemble method to quantify the prediction variations and improve model reproducibility. However, the ensemble method makes multiple predictions given an input, and is computationally expensive especially serving web-scale traffic at inference time.
In this paper, we seek to advance our understanding of prediction variation. We demonstrate that we are able to use neuron activation strength to infer prediction variation. Through empirical experiments on two widely used benchmark datasets Movielens and Criteo, we observed that prediction variations do come from various different sources with randomness, including training data shuffling, and model and embedding parameter random initialization. By adding more randomness sources into model training, we noticed that the ensemble method tends to produce more accurate predictions with higher prediction variations. Last but not least, we demonstrate that neuron activation strength has strong prediction power to infer the ensemble prediction variation. Our approach provides a cheap and simple way for prediction variation estimation, which sets up the foundation and opens up new opportunities for future work on many interesting areas (e.g., model-based reinforcement learning, and active learning) without having to relying on expensive ensemble models.
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Learning to Embed Categorical Features without Embedding Tables for Recommendation
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Tiansheng Yao
Ting Chen
Lichan Hong
KDD (2021)
Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations
Ji Yang
Lichan Hong
Yang Li
Simon Wang
Taibai Xu
WWW '20: Companion Proceedings of the Web Conference 2020April 2020 (2020)
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Learning query and item representations is important for building large scale recommendation systems. In many real applications where there is a huge catalog of items to recommend, the problem of efficiently retrieving top k items given user's query from deep corpus leads to a family of factorized modeling approaches where query and item are jointly embedded into a low-dimensional space. In this paper, we first showcase how to apply a two-tower neural network framework, which is also known as dual encoder in the natural language community, to improve a large-scale, production app recommendation system. Furthermore, we offer a novel negative sampling approach called Mixed Negative Sampling (MNS). In particular, different from commonly used batch or unigram sampling methods, MNS uses a mixture of batch and uniformly sampled negatives to tackle the selection bias of implicit user feedback. We conduct extensive offline experiments in the production dataset and show that MNS outperforms other baseline sampling methods. We also conduct online A/B testing and demonstrate that the two-tower retrieval model based on MNS significantly improves retrieval quality by encouraging more high-quality app installs.
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Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
Ji Yang
Lichan Hong
Lukasz Heldt
Aditee Ajit Kumthekar
Zhe Zhao
Li Wei
RecSys 2019
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Many recommendation systems need to retrieve and score items from a large corpus. A common approach to handle data sparsity and power-law item distribution is to learn item representations from its content features. Apart from many content-aware systems based on matrix factorization, in this paper, we consider a modeling framework with two-tower neural networks where one network called item tower is used to encode a wide variety of item features. Optimizing loss functions calculated from in-batch negatives, which are items sampled in a random batch, is a general recipe of training such two-tower models. However, batch loss is subject to sampling bias which could severely restrict model performance, particularly in the case of power-law distribution. In this work, we present a novel algorithm for estimating item frequency from streaming data. Our main idea is to sketch and estimate item occurrences via gradient descent. Through theoretical analysis and simulations, we show that the proposed algorithm can work without fixed item vocabulary, and is capable of producing unbiased estimation and being adaptive to item distribution change. We then apply the sampling-bias-corrected modeling approach to build a large scale retrieval system called Neural Deep Retrieval (NDR) for YouTube recommendations. The system is deployed to retrieve personalized suggestions from a corpus of tens of millions videos. We demonstrate the effectiveness of sampling bias correction through offline experiments on two real-world datasets. We also conduct live A/B testings to show that the NDR system leads to improved recommendation quality for YouTube.
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Beyond Globally Optimal: Focused Learning for Improved Recommendations
Alex Beutel
Hubert Pham
John Anderson
Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, April 3-7, 2017
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When building a recommender system, how can we ensure that all items are modeled well? Classically, recommender systems are built, optimized, and tuned to improve a global prediction objective, such as root mean squared error. However, as we demonstrate, these recommender systems often leave many items badly-modeled and thus under-served. Further, we give both empirical and theoretical evidence that no single matrix factorization, under current state-of-the-art methods, gives optimal results for each item.
As a result, we ask: how can we learn additional models to improve the recommendation quality for a specified subset of items? We offer a new technique called focused learning, based on hyperparameter optimization and a customized matrix factorization objective. Applying focused learning on top of weighted matrix factorization, factorization machines, and LLORMA, we demonstrate prediction accuracy improvements on multiple datasets. For instance, on MovieLens we achieve as much as a 17% improvement in prediction accuracy for niche movies, cold-start items, and even the most badly-modeled items in the original model.
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Improving User Topic Interest Profiles by Behavior Factorization
Zhe Zhao
Lichan Hong
Proceedings of the 24th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland (2015), pp. 1406-1416
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Many recommenders aim to provide relevant recommendations to users by building personal topic interest profiles and then using these profiles to find interesting contents for the user. In social media, recommender systems build user profiles by directly combining users' topic interest signals from a wide variety of consumption and publishing behaviors, such as social media posts they authored, commented on, +1'd or liked. Here we propose to separately model users' topical interests that come from these various behavioral signals in order to construct better user profiles. Intuitively, since publishing a post requires more effort, the topic interests coming from publishing signals should be more accurate of a user's central interest than, say, a simple gesture such as a +1. By separating a single user's interest profile into several behavioral profiles, we obtain better and cleaner topic interest signals, as well as enabling topic prediction for different types of behavior, such as topics that the user might +1 or comment on, but might never write a post on that topic.
To do this at large scales in Google+, we employed matrix factorization techniques to model each user's behaviors as a separate example entry in the input user-by-topic matrix. Using this technique, which we call "behavioral factorization", we implemented and built a topic recommender predicting user's topical interests using their actions within Google+. We experimentally showed that we obtained better and cleaner signals than baseline methods, and are able to more accurately predict topic interests as well as achieve better coverage.
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