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In this paper, we consider recommender systems with side information in theform of graphs. Existing collaborative filtering algorithms mainly utilize onlyimmediate neighborhood information and have a hard time taking advantage ofdeeper neighborhoods beyond 1-2 hops. The main caveat of exploiting deepergraph information is the rapidly growing time and space complexity whenincorporating information from these neighborhoods. In this paper, we proposeusing Graph DNA, a novel Deep Neighborhood Aware graph encoding algorithm, forexploiting deeper neighborhood information. DNA encoding computes approximatedeep neighborhood information in linear time using Bloom filters, aspace-efficient probabilistic data structure and results in a per-node encodingthat is logarithmic in the number of nodes in the graph. It can be used inconjunction with both feature-based and graph-regularization-basedcollaborative filtering algorithms. Graph DNA has the advantages of beingmemory and time efficient and providing additional regularization when comparedto directly using higher order graph information. We conduct experiments onreal-world datasets, showing graph DNA can be easily used with 4 popularcollaborative filtering algorithms and consistently leads to a performanceboost with little computational and memory overhead.

Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering