Fast context-aware recommendations with factorization machines

@inproceedings{Rendle2011FastCR,
  title={Fast context-aware recommendations with factorization machines},
  author={Steffen Rendle and Zeno Gantner and Christoph Freudenthaler and Lars Schmidt-Thieme},
  booktitle={SIGIR},
  year={2011}
}
The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for context-aware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model. However this method has two drawbacks: (1) its model complexity is exponential in the number of context variables and polynomial in the size of… CONTINUE READING
Highly Influential
This paper has highly influenced a number of papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 258 citations. REVIEW CITATIONS

7 Figures & Tables

Topics

Statistics

020406020112012201320142015201620172018
Citations per Year

259 Citations

Semantic Scholar estimates that this publication has 259 citations based on the available data.

See our FAQ for additional information.