Moustafa Alzantot
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CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation
Keyi Yu
18th ACM Conference on Recommender Systems (RecSys 2024) (2024) (to appear)
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Personalized recommendation requires understanding both the candidate items and user preferences. Traditional collaborative filtering approaches rely on embedding users and items in the same representation space while recent efforts formulate the problem into sequential user activity modeling and future activity prediction tasks. Some of the most recent efforts leverage autoregressive large language models to directly generate the recommendation. This work proposes CALRec, a sequential recommendation framework aligning the generative task based on PaLM-2 LLM with contrastive learning tasks for user/item understanding. To leverage the strong generalization capabilities of the state-of-the-art pretrained LLMs, our input consists of pure texts following differentiable text templates for user inputs and item inputs. We propose novel ways of combining generative loss and contrastive losses in multi-category joint continuous pretraining, followed by domain-specific finetuning. During training, the LLM backbone trains in a two-tower fashion to comprehend users’ consecutive behaviors and descriptions of individual items. Our model outperforms many state-of-the-art baselines significantly especially in ranking tasks. Our systematic ablation study reveals that (i) multi-category pretraining and domain-adaptation finetuning are both important and deliver better performance when combined, and (ii) contrastive alignment further improves the quality among many categories of the Amazon review dataset.
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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)
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.
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