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Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
From complexity to perplexity
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Google scholar citations and google web/url citations: A multi-discipline exploratory analysis
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The probabilistic relevance framework: Bm25 and beyond
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Information theoretic measures for clusterings comparison: is a correction for chance necessary?
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An intuitive proof of the data processing inequality
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Algorithms for hierarchical clustering: an overview
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
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Session-based recommendations with recurrent neural networks
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Categorical reparameterization with gumbel-softmax
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Ms marco: A human-generated machine reading comprehension dataset
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Feature-level deeper self-attention network for sequential recommendation
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Neural discrete representation learning
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Lagging inference networks and posterior collapse in variational autoencoders
He, J., Spokoyny, D., Neubig, G., and Berg-Kirkpatrick, T · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Natural questions: a benchmark for question answering research
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Hierarchical gating networks for sequential recommendation
Ma, C., Kang, P., and Liu, X · 2019
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From doc2query to doctttttquery
Nogueira, R., Lin, J., and Epistemic, A · 2019
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SimCSE: Simple contrastive learning of sentence embeddings
Gao, T., Yao, X., and Chen, D · 2021
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Autoregressive image generation using residual quantization
Lee, D., Kim, C., Kim, S., Cho, M., and Han, W.-S · 2022
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A contrastive pre-training approach to discriminative autoencoder for dense retrieval
Ma, X., Zhang, R., Guo, J., Fan, Y., and Cheng, X · 2022
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Shopping queries dataset: A large-scale ESCI benchmark for improving product search, 2022
Reddy, C. K., Màrquez, L., Valero, F., Rao, N., Zaragoza, H., Bandyopadhyay, S., Biswas, A., Xing, A., and Subbian, K · 2022
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Transformer memory as a differentiable search index
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A neural corpus indexer for document retrieval
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Opitz, J. and Burst, S · 2019
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Language models are unsupervised multitask learners
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P · 2019
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Electra: Pre-training text encoders as discriminators rather than generators
Clark, K., Luong, M.-T., Le, Q. V., and Manning, C. D · 2020
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Overview of the trec 2019 deep learning track
Craswell, N., Mitra, B., Yilmaz, E., Campos, D., and Voorhees, E. M · 2020
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Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., and Yih, W.-t · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P. J., et al · 2020
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Wang, Y., Hou, Y., Wang, H., Miao, Z., Wu, S., Chen, Q., Xia, Y., Chi, C., Zhao, G., Liu, Z., et al · 2022
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Retromae: Pre-training retrieval-oriented language models via masked auto-encoder
Xiao, S., Liu, Z., Shao, Y., and Cao, Z · 2022
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How to index item ids for recommendation foundation models
Hua, W., Xu, S., Ge, Y., and Zhang, Y · 2023
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Large language models on graphs: A comprehensive survey
Jin, B., Liu, G., Han, C., Jiang, M., Ji, H., and Han, J · 2023
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How does generative retrieval scale to millions of passages?
Pradeep, R., Hui, K., Gupta, J., Lelkes, A. D., Zhuang, H., Lin, J., Metzler, D., and Tran, V. Q · 2023
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Recommender systems with generative retrieval
Rajput, S., Mehta, N., Singh, A., Keshavan, R. H., Vu, T., Heldt, L., Hong, L., Tay, Y., Tran, V. Q., Samost, J., et al · 2023
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Learning to tokenize for generative retrieval
Sun, W., Yan, L., Chen, Z., Wang, S., Zhu, H., Ren, P., Chen, Z., Yin, D., de Rijke, M., and Ren, Z · 2023
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Towards universal multi-modal personalization: A language model empowered generative paradigm
Wei, T., Jin, B., Li, R., Zeng, H., Wang, Z., Sun, J., Yin, Q., Lu, H., Wang, S., He, J., et al · 2023
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Scalable and effective generative information retrieval
Zeng, H., Luo, C., Jin, B., Sarwar, S. M., Wei, T., and Zamani, H · 2023
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