Collaborative filtering with temporal dynamics

By Yehuda Koren. 2009

1,798 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Marketing

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Citations in the corpus

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, methodology): “First, as we show quantitatively, the evolution of fashion trends can be abrupt and nonlinear, so that existing temporal models such as timeSVD++ [19] are not immediately appropriate to address the challenge of capturing fashion dynamics.”
Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction (2014, methodology): “fm [24], movie rating prediction for Netflix [12, 13] and Douban [17, 24], and personalized tweet recommendation [2].”
Latent factor models with additive and hierarchically-smoothed user preferences (2013, methodology): “One of the most extensively used techniques in these recommendation systems is the latent factor model and its variants [2] [13] [14] [16] (see Koren et al. [15] for an excellent survey).”
Self-Attentive Sequential Recommendation (2018, methodology): “We also don’t include temporal recommendation methods like TimeSVD++ [16] and RRN [17], which differ in setting from what we consider here.”
Temporal recommendation on graphs via long- and short-term preference fusion (2010, methodology): “[10] Y. Koren.”
Probabilistic factor models for web site recommendation (2011, methodology): “Many methods [7, 13, 17, 19, 22, 23, 24, 32] have been proposed to tackle recommendation or collaborative filtering problems in the literature.”
Opinion Fraud Detection in Online Reviews by Network Effects (2013, background): “Related to ratings on products, work on recommender systems (Koren 2009; Menon and Elkan 2011), aim for best prediction of future user ratings, but do not address the fraud problem.”
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization (2010, methodology): “Temporal modeling has been largely neglected in the collaborative filtering community until Koren [12] proposed their award winning algorithm timeSVD++.”
Ratings meet reviews, a combined approach to recommend (2014, background): “There are extensive investigations on collaborative filtering, from neighborhood-based methods [27, 11] to model-based methods [9, 26].”
Long and Short-Term Recommendations with Recurrent Neural Networks (2017, background): “Some, like time-SVD++, incorporate the timestamp of ratings in order to improve ratings prediction [12], but the use of the sequential nature of interactions between users and items has been poorly studied.”
Circle-based recommendation in online social networks (2012, background): “predict users’ interests by mining user rating history data [1], [2], [4], [6], [16], [23] and [24].”
Modeling and predicting behavioral dynamics on the web (2012, methodology): “[13], who used matrix factorization to model user biases, item biases, and user preferences over time.”
Time-Sensitive Recommendation From Recurrent User Activities (2015, background): “However, since most models in literature are particularly optimized for predicting ratings [16, 23, 15, 3, 25, 13, 21],”
Fast Matrix Factorization for Online Recommendation with Implicit Feedback (2016, methodology): “Note that this basic model subsumes the biased MF [14], commonly used in modeling explicit ratings:”