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Generalized framework for personalized recommendations in agent networks

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Abstract

An agent network can be modeled as a directed weighted graph whose vertices represent agents and edges represent a trust relationship between the agents. This article proposes a new recommendation approach, dubbed LocPat, which can recommend trustworthy agents to a requester in an agent network. We relate the recommendation problem to the graph similarity problem, and define the similarity measurement as a mutually reinforcing relation. We understand an agent as querying an agent network to which it belongs to generate personalized recommendations. We formulate a query into an agent network as a structure graph applied in a personalized manner that reflects the pattern of relationships centered on the requesting agent. We use this pattern as a basis for recommending an agent or object (a vertex in the graph). By calculating the vertex similarity between the agent network and a structure graph, we can produce a recommendation based on similarity scores that reflect both the link structure and the trust values on the edges. Our resulting approach is generic in that it can capture existing network-based approaches merely through the introduction of appropriate structure graphs. We evaluate different structure graphs with respect to two main kinds of settings, namely, social networks and ratings networks. Our experimental results show that our approach provides personalized and flexible recommendations effectively and efficiently based on local information.

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Authors and Affiliations

  1. North Carolina State University, Raleigh, USA

    Chung-Wei Hang & Munindar P. Singh

Authors
  1. Chung-Wei Hang
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  2. Munindar P. Singh
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Correspondence to Chung-Wei Hang.

Additional information

A preliminary version of this manuscript, “Trust-Based Recommendation Based on Graph Similarity,” was presented at the AAMAS 2010 Workshop on Trust in Agent Societies and appears in the unpublished workshop notes. Sects. 1–3 of this manuscript are based on that paper. This manuscript incorporates substantial revisions and extensions to the formal models and techniques. The evaluation and results are new.

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Hang, CW., Singh, M.P. Generalized framework for personalized recommendations in agent networks. Auton Agent Multi-Agent Syst 25, 475–498 (2012). https://doi.org/10.1007/s10458-011-9186-1

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