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Collaborative Ranking

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Temporal Collaborative Ranking Via Personalized Transformer

Cho-Jui Hsieh, Shuqing Li, Liwei Wu, J. Sharpnack•Wed Aug 14 2019

A Personalized Transformer (SSE-PT) model, inspired by the popular Transformer model in natural languages processing, which can handle extremely long sequences and outperform SASRec in ranking results with comparable training speed, striking a balance between performance and speed requirements.

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Paper Graph

Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons

I. Dhillon, Dohyung Park, Joe Neeman, Jin Zhang, S. Sanghavi•Sun Jul 05 2015

A large-scale non-convex implementation of AltSVM is developed that trains a factored form of the matrix via alternating minimization, and scales and parallelizes very well to large problem settings.

79 0

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Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

Anh Tuan Luu, Yi Tay, S. Hui•Sun Jul 16 2017

Qualitative studies demonstrate evidence that the proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback, ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.

316 0

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SQL-Rank: A Listwise Approach to Collaborative Ranking

Cho-Jui Hsieh, Liwei Wu, J. Sharpnack•Tue Feb 27 2018

A theoretical framework for analyzing listwise ranking methods based on a novel representation theory for the permutation model is developed and derived, which derives asymptotic statistical rates as the number of users and items grow together.

45 0

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SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

Chao Wang, Hengshu Zhu, Chen Zhu, Chuan Qin, Hui Xiong•Sat Feb 22 2020

This paper proposes a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system and aims at maximizing the posterior probability of novel set Wise preference comparisons.

48 0

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Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning

Junchi Yan, Xiaokang Yang, Chao Chen, Dongsheng Li, Hanchi Huang•Mon May 17 2021

The spectral graph matrix completion (SGMC) method is proposed, which can recover the underlying matrix in distributed systems by filtering the noisy data in the graph frequency domain by developing polynomial and sparse frequency filters to remedy the accuracy loss caused by the approximations.

15 0

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Advances in Collaborative Filtering and Ranking

Liwei Wu•Wed Feb 26 2020

This dissertation, covering some recent advances in collaborative filtering and ranking, introduces personalization for the state-of-the-art sequential recommendation model with the help of SSE and discusses the new regularization technique Stochastic Shared Embeddings.

7 0

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Collaborative Ranking | State-of-the-Art