Efficient filtering techniques for cosine similarity joins

Similarity join, an operation that finds all pairs of similar objects in a large collection, is widely used to solve various problems in many application domains. Existing similarity join algorithms use filtering techniques to avoid unnecessary similarity computation based on inverted index. However, they are inefficient in filtering out dissimilar pairs when an aggregate weighted similarity function, such as cosine similarity, is used to quantify similarity values between objects. This is mainly because of loose filtering conditions the existing algorithms adopt. In this paper, we formalize filtering conditions adopted by the previous algorithms and contrive new similarity upper bounds that can be used to make tighter filtering conditions for cosine similarity joins over weight vectors. Our algorithm efficiently filters out dissimilar pairs by exploiting the new similarity upper bounds. We demonstrate that our algorithm outperforms a state-of-the-art algorithm by performing empirical evaluations on large- scale datasets. In addition, we present that our algorithm can be extended to Dice and Tanimito similarity joins over weight vectors.