Fast context-aware recommendations with factorization machines

By Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, Lars Schmidt-Thieme. 2011

624 citations recorded by the source corpus.

Subfields: Computational Mathematics, Information Systems, Transportation

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Citations in the corpus

Neural Collaborative Filtering (2017, neutral): “as context-aware [28, 1], content-based [3], and neighborbased [26].”
PrivCheck: privacy-preserving check-in data publishing for personalized location based services (2016, methodology): “By aggregating and discretizing the context (i.e., time slots and location grid cells) of check-in data [50, 46] to build a user-context-activity tensor, many popular approaches leverages tensor factorization techniques for user preference prediction [33, 20, 43].”
Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation (2016, background): “Another approach is to learn latent factors for both CF and content features, known as Factorization Machines [25].”
Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks (2016, methodology): “This method is a simpliﬁed version of Factorization Machines[7], where only interactions between variables of the session and the item are allowed for ﬁnal estimation.”
Data Fusion by Matrix Factorization (2013, background): “The drawback of their and similar approaches [27], [31], [32] for r-ary tensors is that in higher dimensions”
Factorization Machines with libFM (2012, methodology): “[Rendle et al. 2011]. y (x)= g.(x)+ .h.(x) .. .8, (6) with .”
Recommender Systems (2016, positive): “A closely related ranking method, referred to as Pairwise Interaction Tensor Factorization (PITF) [496], has also been used for tag recommendation.”
ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation (2017, methodology): “RNN based methods are studied for recommendation in academic ﬁelds in recent years, which builds the recommendation context directly from the behaviors for each user (Rendle et al. 2011; Hariri, Mobasher, and Burke 2012; Wu et al. 2016).”
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (2020, background): “With the prevalence of linked graph data in real applications, graph-based models are becoming increasingly important in recommendation; by explicitly exploiting the relations among entities in the predictive model, they are advantageous to traditional supervised learning scheme like factorization m”
CORE: Context-Aware Open Relation Extraction with Factorization Machines (2015, methodology): “To do so, we leverage factorization machines (Rendle et al., 2011; Rendle, 2012), which have been successfully applied to exploit contextual information in the context of recommender systems.”
TFMAP: optimizing MAP for top-n context-aware recommendation (2012, methodology): “For this reason, we use another dataset, Food dataset [22], which has also been used in the work on FMs [25].”
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (2018, methodology): “A well-known example is Factorization Machines (FM) [23], which were proposed to mainly capture the first- and second-order feature interactions and have been proved effective for many tasks in recommender systems [24, 25].”
Collaborative Filtering beyond the User-Item Matrix (2014, background): “FM has been demonstrated to successfully incorporate various information sources associated with the U-I interactions and enable context-aware recommendation at a low computational cost [Rendle et al. 2011].”
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data (2018, methodology): “Various algorithms such as collaborative filtering [4, 18, 28, 29, 37] and factorization based methods [18, 22, 27] have been proposed to optimize those metrics.”
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback (2012, methodology): “A further improvement was proposed by Rendle et al [21], where the computational complexity was reduced so that their method scales linearly both with the number of explicit ratings and with the number of features.”
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols (2013, methodology): “Another example of categorical time-aware model is given in Rendle et al. (2011), where factorization machines (FMs) (Rendle 2011) were used to combine continuous and categorical time information.”
Fast Matrix Factorization for Online Recommendation with Implicit Feedback (2016, methodology): “To make our method more applicable to real-world settings, we plan to encode side information such as user social contexts [8] and reviews [9] by extending eALS to more generic models, such as collective factorization [11] and Factorization machines [26].”