OrdRec: an ordinal model for predicting personalized item rating distributions

By Yehuda Koren, Joseph Sill. 2011

197 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Transportation

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Citations in the corpus

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017, neutral): “Further ranking loss function for collaborative filtering were introduced in [18] [15] and [12].”
Model compression via distillation and quantization (2018, methodology): “…idea of optimizing the locations of quantization points during the learning process, which we use in differentiable quantization, has been used previously in Lan et al. (2014); Koren & Sill (2011); Zhang et al. (2017), although in the different context of matrix completion and recommender systems.”
Learning to rank for recommender systems (2013, methodology): “Then, we review three types of LtR techniques, i.e., point-wise, pair-wise and list-wise LtR. CF methods that learn a ranking model based on the preference scores of individual items can are considered point-wise ranking methods [3].”
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (2012, methodology): “For this reason, OrdRec may not be suitable for the scenarios with only binary relevance data.”
Recommender Systems (2012, background): “Recent evidence [96] to some extent supports the latter ansatz.”
Matrix recovery from quantized and corrupted measurements (2014, background): “Corresponding theoretical results for MC have shown that only O(r n polylog(n)) randomly observed entries in Ωobs are required to exactly recover the n×n, rank-r matrix L (see, e.g., [1]).”
Ratings meet reviews, a combined approach to recommend (2014, methodology): “This is typically a very strong baseline model in collaborative filtering [10, 23].”
Evaluating Recommender Systems with User Experiments (2015, unknown): “[74]).”
Learning to personalize trending image search suggestion (2014, background): “6 Many other proposed MF techniques to fit different rank-oriented optimization goals [18, 27, 30, 34] can also be adopted.”