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Nikhil Mehta

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2 papers

1 author row

NeurIPS Conference 2023 Conference Paper

Recommender Systems with Generative Retrieval

Shashank Rajput
Nikhil Mehta
Anima Singh
Raghunandan Hulikal Keshavan
Trung Vu
Lukasz Heldt
Lichan Hong
Yi Tay

Modern recommender systems perform large-scale retrieval by embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search to select top candidates given a query embedding. In this paper, we propose a novel generative retrieval approach, where the retrieval model autoregressively decodes the identifiers of the target candidates. To that end, we create semantically meaningful tuple of codewords to serve as a Semantic ID for each item. Given Semantic IDs for items in a user session, a Transformer-based sequence-to-sequence model is trained to predict the Semantic ID of the next item that the user will interact with. We show that recommender systems trained with the proposed paradigm significantly outperform the current SOTA models on various datasets. In addition, we show that incorporating Semantic IDs into the sequence-to-sequence model enhances its ability to generalize, as evidenced by the improved retrieval performance observed for items with no prior interaction history.

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AAAI Conference 2020 Conference Paper

Graph Representation Learning via Ladder Gamma Variational Autoencoders

Arindam Sarkar
Nikhil Mehta
Piyush Rai

We present a probabilistic framework for community discovery and link prediction for graph-structured data, based on a novel, gamma ladder variational autoencoder (VAE) architecture. We model each node in the graph via a deep hierarchy of gamma-distributed embeddings, and deﬁne each link probability via a nonlinear function of the bottom-most layer’s embeddings of its associated nodes. In addition to leveraging the representational power of multiple layers of stochastic variables via the ladder VAE architecture, our framework offers the following beneﬁts: (1) Unlike existing ladder VAE architectures based on real-valued latent variables, the gammadistributed latent variables naturally result in non-negativity and sparsity of the learned embeddings, and facilitate their direct interpretation as membership of nodes into (possibly multiple) communities/topics; (2) A novel recognition model for our gamma ladder VAE architecture allows fast inference of node embeddings; and (3) The framework also extends naturally to incorporate node side information (features and/or labels). Our framework is also fairly modular and can leverage a wide variety of graph neural networks as the VAE encoder. We report both quantitative and qualitative results on several benchmark datasets and compare our model with several state-of-the-art methods.

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