Sketching Techniques for Collaborative Filtering

By Yoram Bachrach, Ely Porat, Jeffrey S. Rosenschein. 2009

39 citations recorded by the source corpus.

Subfields: Recommender Systems, Algorithms & Complexity

How researchers see this paper

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

In Recommender Systems

1. Ping Li
2. Anshumali Shrivastava

In Algorithms & Complexity

1. Anshumali Shrivastava
2. Ping Li
3. Margo I. Seltzer
4. Behnam Neyshabur
5. Nati Srebro
6. Heng Tao Shen
7. Jingkuan Song
8. Jingdong Wang

Citations in the corpus

• UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats (2020, methodology): “We instead use locality sensitive hashing [59], also called similarity-preserving data sketching [124], which is commonly used in classifying [5] and filtering [12] high-cardinality data streams.”
• Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment (2015, background): “Later, because of its locality sensitive property [22], minhash became a widely used hash function for creating hash buckets leading to efficient algorithms for numerous applications including spam detection [6], collaborative filtering [4], news personalization [15], compressing social networks [13”
• Asymmetric Minwise Hashing (2014, background): “…function for creating hash buckets leading to efficient algorithms for numerous applications including spam detection [6], collaborative filtering [4], news personalization [15], compressing social networks [13], graph sampling [14], record linkage [24], duplicate detection [20], all pair…”