Sketch 'Em All: Fast Approximate Similarity Search for Dynamic Data Streams

Recommender systems are an integral part of many web applica- tions. With increasingly larger user bases, scalability has become an important issue. Many of the most scalable algorithms with respect to both space and running times are based on locality-sensitive hashing (LSH). However, a significant drawback is that these meth- ods are only able to handle insertions to user profiles and tend to perform poorly when items may be removed. We initiate the study of scalable locality-sensitive hashing for dynamic input. Specifi- cally, using the Jaccard index as similarity measure, we design (1) a sketching algorithm for similarity estimation via a black box re- duction to ℓ0 norm estimation and (2) a locality sensitive hashing scheme maintainable in fully dynamic data streams that quickly filters out low-similarity pairs. Our algorithms have little to no overhead in terms of running time compared to previous LSH ap- proaches for the insertion only case, and drastically outperform previous algorithms in case of deletion