Abstract
We present NearBucket-LSH, an effective algorithm for similarity search in large-scale distributed online social networks organized as peer-to-peer overlays. As communication is a dominant consideration in distributed systems, we focus on minimizing the network cost while guaranteeing good search quality. Our algorithm is based on Locality Sensitive Hashing (LSH), which limits the search to collections of objects, called buckets, that have a high probability to be similar to the query. More specifically, NearBucket-LSH employs an LSH extension that searches in near buckets, and improves search quality but also significantly increases the network cost. We decrease the network cost by considering the internals of both LSH and the P2P overlay, and harnessing their properties to our needs. We show that our NearBucket-LSH increases search quality for a given network cost compared to previous art. In many cases, the search quality increases by more than \(50\,\%\).
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Notes
- 1.
Note that in a general c-dimensional CAN of N nodes, the expected routing length is \(c/4\left( N^{1/c}\right) \) [25], which equals k/2 for \(c=k\) and \(N=2^k\).
- 2.
We transform cosine similarity into angular similarity and then apply the success probability formulas.
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Naama Kraus is grateful to the Hasso-Plattner-Institut (HPI) for the scholarship for doctoral studies.
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Kraus, N., Carmel, D., Keidar, I., Orenbach, M. (2016). NearBucket-LSH: Efficient Similarity Search in P2P Networks. In: Amsaleg, L., Houle, M., Schubert, E. (eds) Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science(), vol 9939. Springer, Cham. https://doi.org/10.1007/978-3-319-46759-7_18
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