Sparse linear methods with side information for top-n recommendations

154 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.”
Embarrassingly Shallow Autoencoders for Sparse Data (2019, background): “Also note that extensions to Slim like cSLIM [17], can be turned into an analogous extension of .”
A Comprehensive Survey of Neighborhood-based Recommendation Methods (2011, methodology): “In [51], item reviews in the form of short texts were used as side information in the models described above.”
Recommender Systems (2016, unknown): “The technique can also be hybridized [456] with side-information (cf.”
Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios (2017, background): “As a matter of fact, adding side information to a collaborative filtering approach has proven to be more effective while computing recommendations to the end user [18].”
Sound and Music Recommendation with Knowledge Graphs (2016, methodology): “SLIM [Ning and Karypis 2012] uses a Sparse Linear method for learning a sparse aggregation coefﬁcient matrix.”