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.
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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
The 2nd International Workshop on Online and Adaptive Recommender Systems (2022)
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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.
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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)
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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.
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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
Tao Wu
Wen Li
CIKM (2020)
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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.
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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)
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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.
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Threshold query optimization for uncertain data
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Yinian Qi
Rohit Jain
Sunil Prabhakar
Special Interest Group on Management of Data (SIGMOD) (2010)
