document: Fast query execution for retrieval models based on path-constrained random walks Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph , with typed nodes representing documents , terms , and metadata , and labeled edges representing the relationships between them . Recent work has shown that the accuracy of the widely-used random-walk-based proximity measures can be improved by supervised learning - in particular , one especially effective learning technique is based on Path-Constrained Random Walks ( PCRW ) , in which similarity is defined by a learned combination of constrained random walkers , each constrained to follow only a particular sequence of edge labels away from the query nodes . The PCRW based method significantly outperformed unsupervised random walk based queries , and models with learned edge weights . Unfortunately , PCRW query systems are expensive to evaluate . In this study we evaluate the use of approximations to the computation of the PCRW distributions , including fingerprinting , particle filtering , and truncation strategies . In experiments on several recommendation and retrieval problems using two large scientific publications corpora we show speedups of factors of 2 to 100 with little loss in accuracy .
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