961 citations
- Cornell UniversityUS5 papers
- University of California, BerkeleyUS4 papers
- Google (United States)US3 papers
- Princeton UniversityUS3 papers
- Stanford UniversityUS3 papers
- Université Paris CitéFR3 papers
- California Institute of TechnologyUS2 papers
- Florida State UniversityUS2 papers
- Guangxi UniversityCN2 papers
- Jet Propulsion LaboratoryUS2 papers
- Lawrence Berkeley National LaboratoryUS2 papers
- Mathématiques Appliquées à Paris 5FR2 papers
Showing cs.IRShow all
3 papers · 1 filter
cs.IR2019★ 3 cited
Markov Random Fields for Collaborative Filtering
Harald Steck
In this paper, we model the dependencies among the items that are recommended to a user in a collaborative-filtering problem via a Gaussian Markov Random Field (MRF). We build upon…
cs.IR2019★ 272 cited
Embarrassingly Shallow Autoencoders for Sparse Data
Harald Steck
Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show tha…
cs.IR2019★ 4 cited
Challenges in Search on Streaming Services: Netflix Case Study
Sudarshan Lamkhede, Sudeep Das
We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search…