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Sarvjeet Singh

Sarvjeet Singh

Sarvjeet Singh is a Principal Engineer and Director of Engineering at Google. With over 15 years of experience, he is a leader in AI and applied AI research. His work has shaped products across Google, including Ads (where he pioneered Target CPA and ECPC bidding solutions), YouTube, and Google's recommendation systems. Currently, Sarvjeet focuses on enhancing user agency in AI systems.
Authored Publications
Google Publications
Other Publications
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    Data Bootstrapping for Interactive Recommender Systems
    Ajay Joshi
    Ajit Apte
    Anand Kesari
    Anushya Subbiah
    Dima Kuzmin
    John Anderson
    Li Zhang
    Marty Zinkevich
    Sally Goldman
    Steffen Rendle
    The 2nd International Workshop on Online and Adaptive Recommender Systems (2022)
    Preview abstract Modifying recommender systems for new kinds of user interactions is costly and exploration is slow since machine learning models can be trained and evaluated on live data only after a product supporting these new interactions is deployed. Our data bootstrapping approach moves the task of developing models for new interactions into the input representation allowing a standard machine learning model (e.g. a transformer model) to be used to train a model capturing the new interactions. More specifically, we use data obtained from a launched system to generate simulated data that includes the new interactions options. This approach helps accelerate model and algorithm development, and reduce the time to launch new interaction experiences. We present machine learning methods designed specifically to work well with limited and noisy data produced via data bootstrapping. View details
    Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
    Ajit Apte
    Ambarish Jash
    Amol H Wankhede
    Ankit Kumar
    Ayooluwakunmi Jeje
    Dima Kuzmin
    Ellie Ka In Chio
    Harry Fung
    Jon Effrat
    Nitin Jindal
    Pei Cao
    Senqiang Zhou
    Sukhdeep S. Sodhi
    Tameen Khan
    Tarush Bali
    KDD (2021)
    Preview abstract As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, Mondegreen, to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts. View details
    Zero-Shot Transfer Learning for Query-Item Cold Start in Search Retrieval and Recommendations
    Ankit Kumar
    Cosmo Du
    Dima Kuzmin
    Ellie Chio
    John Roberts Anderson
    Li Zhang
    Nitin Jindal
    Pei Cao
    Ritesh Agarwal
    Steffen Rendle
    Tao Wu
    Wen Li
    CIKM (2020)
    Preview abstract Most search retrieval and recommender systems predict top-K items given a query by learning directly from a large training set of (query, item) pairs, where a query can include natural language (NL), user, and context features. These approaches fall into the traditional supervised learning framework where the algorithm trains on labeled data from the target task. In this paper, we propose a new zero-shot transfer learning framework, which first learns representations of items and their NL features by predicting (item, item) correlation graphs as an auxiliary task, followed by transferring learned representations to solve the target task (query-to-item prediction), without having seen any (query, item) pairs in training. The advantages of applying this new framework include: (1) Cold-starting search and recommenders without abundant query-item data; (2) Generalizing to previously unseen or rare (query, item) pairs and alleviating the "rich get richer" problem; (3) Transferring knowledge of (item, item) correlation from domains outside of search. We show that the framework is effective on a large-scale search and recommender system. View details
    Video WatchTime and Comment Sentiment: Experience from YouTube
    Bo Fu
    Pei Cao
    Rong Yang
    Proceedings of the Fourth IEEE Workshop on Hot Topics in Web Systems and Technologies (2016)
    Preview abstract Video watching is now an indispensable part of the general public media consumption, yet very little is known about the relationship between how users interact with each other and how that affects video consumption patterns. In this paper, we explore the relationship between user commenting behavior and how that might or might not be predictive of video consumption patterns such as watch time. Contrary to recent findings, we found that video watch time is correlated with the positive sentiment expressed in the comments of YouTube videos. More precisely, videos with more positive sentiment on average in the comments are more likely to be watched longer; while videos with negative comment sentiment on average are more likely to have shorter watch durations. These results suggest that users prefer videos that evoke positive emotional responses. If the findings here generalizes to other social media, this result suggests a motivational design finding that is useful for other system designers. View details
    Threshold query optimization for uncertain data
    Yinian Qi
    Rohit Jain
    Sunil Prabhakar
    Special Interest Group on Management of Data (SIGMOD) (2010)
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