{"list":"other","total":51,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1702.08734","title":"Billion-scale similarity search with GPUs","created":"2017-02-28","cited_by_count":2497},{"id":"2001.04451","title":"Reformer: The Efficient Transformer","created":"2020-01-13","cited_by_count":1668},{"id":"1808.09781","title":"Self-Attentive Sequential Recommendation","created":"2018-08-20","cited_by_count":1184},{"id":"1904.06690","title":"BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer","created":"2019-04-14","cited_by_count":964},{"id":"1809.07426","title":"Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding","created":"2018-09-19","cited_by_count":476},{"id":"2010.01028","title":"Hard Negative Mixing for Contrastive Learning","created":"2020-10-02","cited_by_count":402},{"id":"2203.13366","title":"Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)","created":"2022-03-24","cited_by_count":402},{"id":"1811.00855","title":"Session-based Recommendation with Graph Neural Networks","created":"2018-11-01","cited_by_count":312},{"id":"2008.07873","title":"S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization","created":"2020-08-18","cited_by_count":311},{"id":"2002.11296","title":"Sparse Sinkhorn Attention","created":"2020-02-26","cited_by_count":251},{"id":"1711.04725","title":"Neural Attentive Session-based Recommendation","created":"2017-11-13","cited_by_count":237},{"id":"1802.08665","title":"Learning Latent Permutations with Gumbel-Sinkhorn Networks","created":"2018-02-23","cited_by_count":175},{"id":"2205.08084","title":"M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems","created":"2022-05-17","cited_by_count":152},{"id":"2011.01731","title":"RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms","created":"2020-11-03","cited_by_count":138},{"id":"2206.05941","title":"Towards Universal Sequence Representation Learning for Recommender Systems","created":"2022-06-13","cited_by_count":121},{"id":"1606.00185","title":"A Survey on Learning to Hash","created":"2016-06-01","cited_by_count":119},{"id":"2202.13556","title":"Filter-enhanced MLP is All You Need for Sequential Recommendation","created":"2022-02-28","cited_by_count":96},{"id":"2106.14226","title":"Sequential Recommendation with Graph Neural Networks","created":"2021-06-27","cited_by_count":89},{"id":"2202.06200","title":"Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning","created":"2022-02-13","cited_by_count":86},{"id":"1804.06769","title":"CoNet: Collaborative Cross Networks for Cross-Domain Recommendation","created":"2018-04-18","cited_by_count":81},{"id":"2103.01696","title":"Cross-Domain Recommendation: Challenges, Progress, and Prospects","created":"2021-03-02","cited_by_count":74},{"id":"1909.02107","title":"Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems","created":"2019-09-04","cited_by_count":68},{"id":"1602.07017","title":"A survey of sparse representation: algorithms and applications","created":"2016-02-23","cited_by_count":58},{"id":"2105.08318","title":"Zero-Shot Recommender Systems","created":"2021-05-18","cited_by_count":46},{"id":"2206.07351","title":"RecBole 2.0: Towards a More Up-to-Date Recommendation Library","created":"2022-06-15","cited_by_count":39},{"id":"2204.11067","title":"CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space","created":"2022-04-23","cited_by_count":37},{"id":"1908.09756","title":"Differentiable Product Quantization for End-to-End Embedding Compression","created":"2019-08-26","cited_by_count":36},{"id":"2108.00644","title":"Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance","created":"2021-08-02","cited_by_count":35},{"id":"2009.13724","title":"One Person, One Model, One World: Learning Continual User Representation without Forgetting","created":"2020-09-29","cited_by_count":30},{"id":"2110.05789","title":"Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval","created":"2021-10-12","cited_by_count":27},{"id":"2206.06190","title":"TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback","created":"2022-06-13","cited_by_count":27},{"id":"2112.00999","title":"Contrastive Cross-domain Recommendation in Matching","created":"2021-12-02","cited_by_count":25},{"id":"2002.08530","title":"Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems","created":"2020-02-20","cited_by_count":24},{"id":"2010.01494","title":"PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision","created":"2020-10-04","cited_by_count":22},{"id":"2109.05929","title":"Cross-Market Product Recommendation","created":"2021-09-13","cited_by_count":22},{"id":"1909.05475","title":"Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation","created":"2019-09-12","cited_by_count":19},{"id":"2102.10989","title":"UPRec: User-Aware Pre-training for Recommender Systems","created":"2021-02-22","cited_by_count":16},{"id":"2111.10093","title":"RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation","created":"2021-11-19","cited_by_count":16},{"id":"2006.14827","title":"Memory-efficient Embedding for Recommendations","created":"2020-06-26","cited_by_count":15},{"id":"1805.05361","title":"NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing","created":"2018-05-14","cited_by_count":11},{"id":"1806.09464","title":"Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations","created":"2018-06-21","cited_by_count":11},{"id":"2205.10775","title":"Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation","created":"2022-05-22","cited_by_count":8},{"id":"2105.14068","title":"Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation","created":"2021-05-28","cited_by_count":7},{"id":"1905.03752","title":"Compositional Coding for Collaborative Filtering","created":"2019-05-09","cited_by_count":6},{"id":"2206.02323","title":"ID-Agnostic User Behavior Pre-training for Sequential Recommendation","created":"2022-06-06","cited_by_count":5}],"next_cursor":null}