Whom should I trust?: the impact of key figures on cold start recommendations

ABSTRACT

Generating adequate recommendations for newcomers is a hard problem for a recommender system (RS) due to lack of detailed user profiles and social preference data. Empirical evidence suggests that the incorporation of a trust network among the users of the RS can leverage such 'cold start' (CS) recommendations. Hence, new users should be encouraged to connect to the network as soon as possible. But whom should new users connect to? Given the impact this choice has on the delivered recommendations, it is critical to guide newcomers through this early stage connection process. In this paper, we identify key figures in the trust network (in particular mavens, connectors and frequent raters) and investigate their influence on the coverage and accuracy of a collaborative filtering RS. Using a dataset from Epinions.com, we demonstrate that the generated recommendations for new user are more beneficial if they connect to an identified key figure compared to a random user.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Human Factors

Keywords:
cold start problem, recommender system, trust network