Latent factor models with additive and hierarchically-smoothed user preferences

39 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, methodology): “Of particular interest, feature-based matrix factorization techniques have been proposed [3,4,27,28,30] and efficient tools (e.”
Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles (2015, methodology): “Inference is intractable and we resort to a stochastic MCEM algorithm [22], an inference method that alternates between Gibbs sampling for content variables Ω (keeping Θ ﬁxed) and gradient ascent to estimate latent oﬀsets Θ (Ω ﬁxed).”
The use of machine learning algorithms in recommender systems: A systematic review (2015, background): “Today, RSs are used in many information-based companies such as Google [35], Twitter [4], LinkedIn [50], and Netflix [59].”
Up next: retrieval methods for large scale related video suggestion (2014, background): “It is important to note the connection between this work and the ongoing research on hybrid recommender systems [4, 10, 15, 31].”
Improving pairwise learning for item recommendation from implicit feedback (2014, methodology): “…systems use BPR for learning, including tensor factorization models for tag recommendation [15], relation extraction [17], sequential shopping recommender with taxonomies [8], focused matrix factorization for advertisement [7], hierarchical latent factor models [1] or co-factorization machines [5].”