Efficient retrieval of recommendations in a matrix factorization framework

By Noam Koenigstein, Parikshit Ram, Yuval Shavitt. 2012

96 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Information Retrieval & Search

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Yoshua Bengio
2. Stefano Ermon
3. Luc Van Gool
4. Dacheng Tao
5. Ming-Hsuan Yang
6. Trevor Darrell
7. Jitendra Malik
8. Xiaogang Wang

In Information Systems

1. Anshumali Shrivastava
2. Ping Li
3. Zhe Zhao
4. Noam Koenigstein
5. Ed H. Chi
6. Lichan Hong
7. Stefano Ermon
8. Stephen O. Mussmann

In Information Retrieval & Search

1. Anshumali Shrivastava
2. Ping Li
3. Stephen O. Mussmann
4. Stefano Ermon
5. Pascal Vincent
6. Yoshua Bengio
7. Hugo Larochelle
8. Matei A. Zaharia

Citations in the corpus

• Quantization based Fast Inner Product Search (2015, neutral): “In [13, 16], the authors described a procedure to modify tree-based search to adapt to MIPS criterion.”
• Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) (2014, methodology): “, ∥vj∥2, which often has a wide range in practice [13].”
• Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces (2014, methodology): “Further, the results indicate that our method yields higher quality recommendations than the IP-trees approach [13].”
• Factorized Deep Retrieval and Distributed TensorFlow Serving (2018, background): “, [2, 4, 7, 9], for approximate maximum inner product search (MIPS) problems.”
• Sampling-bias-corrected neural modeling for large corpus item recommendations (2019, background): “, [2, 10, 25], for approximate maximum inner product search (MIPS) problems.”
• Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS) (2014, methodology): “In this work we present the first provably fast algorithm for MIPS, which was considered hard [21, 15].”
• To Index or Not to Index: Optimizing Exact Maximum Inner Product Search (2017, methodology): “[18], who proposed to cluster users via spherical clustering to perform approximate top-K queries.”
• Learning and Inference via Maximum Inner Product Search (2016, background): “One class of approaches are space-partitioning methods (Ram & Gray, 2012; Koenigstein et al., 2012), similar to kd trees (Friedman & Tukey, 1974).”
• Clustering is Efficient for Approximate Maximum Inner Product Search (2015, methodology): “In recommendation systems (Koenigstein et al., 2012; Bachrach et al., 2014), users and items to be recommended are represented as vectors that are learnt at training time based on the user-item rating matrix.”