Network based recommendation systems leverage the topology of the underlying graph and the current user context to rank objects in the database. Random-walk based techniques, such as PageRank, encode the structure of the graph in the form of a transition matrix of a stochastic process from which the significances of the nodes in the graph are inferred. Personalized PageRank (PPR) techniques complement this with a seed node set which serves as the personalization context. In this paper, we note (and experimentally show) that PPR algorithms that do not differentiate among the seed nodes may not properly rank nodes in situations where the seed set is incomplete and/or noisy. To tackle this problem, we propose alternative robust personalized PageRank (RPR) strategies, which are insensitive to noise in the set of seed nodes and in which the rankings are not overly biased towards the seed nodes. In particular, we show that novel teleportation discounting and seed-set maximal PPR techniques help eliminate harmful bias of individual seed nodes and provide effective seed differentiation to lead to more accurate rankings.

"Can you really trust that seed?": Reducing the impact of seed noise in personalized PageRank.

SAPINO, Maria Luisa
2014-01-01

Abstract

Network based recommendation systems leverage the topology of the underlying graph and the current user context to rank objects in the database. Random-walk based techniques, such as PageRank, encode the structure of the graph in the form of a transition matrix of a stochastic process from which the significances of the nodes in the graph are inferred. Personalized PageRank (PPR) techniques complement this with a seed node set which serves as the personalization context. In this paper, we note (and experimentally show) that PPR algorithms that do not differentiate among the seed nodes may not properly rank nodes in situations where the seed set is incomplete and/or noisy. To tackle this problem, we propose alternative robust personalized PageRank (RPR) strategies, which are insensitive to noise in the set of seed nodes and in which the rankings are not overly biased towards the seed nodes. In particular, we show that novel teleportation discounting and seed-set maximal PPR techniques help eliminate harmful bias of individual seed nodes and provide effective seed differentiation to lead to more accurate rankings.
2014
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM14)
Beijing
17-20 Aug. 2014
Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM14)
IEEE Computer Society
216
223
9781479958764
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6921528
Shengyu Huang; Xinsheng Li; K. Selçuk Candan; Maria Luisa Sapino
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/154835
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