TITLE:
Alleviating the Cold Start Problem in Recommender Systems Based on Modularity Maximization Community Detection Algorithm
AUTHORS:
S. Vairachilai, M. K. Kavithadevi, M. Raja
KEYWORDS:
Collaborative Recommender Systems, Cold Start Problem, Community Detection, Pearson Correlation Coefficient
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.8,
June
7,
2016
ABSTRACT: Recommender system (RS)
has become a very important factor in many eCommerce sites. In our daily life,
we rely on the recommendation from other persons either by word of mouth,
recommendation letters, movie, item and book reviews printed in newspapers,
etc. The typical Recommender Systems are software tools and techniques that
provide support to people by identifying interesting products and services in
online store. It also provides a recommendation for certain users who search
for the recommendations. The most important open challenge in Collaborative
filtering recommender system is the cold start problem. If the adequate or
sufficient information is not available for a new item or users, the
recommender system runs into the cold start problem. To increase the usefulness
of collaborative recommender systems, it could be desirable to eliminate the
challenge such as cold start problem. Revealing the community structures is
crucial to understand and more important with the increasing popularity of
online social networks. The community detection is a key issue in social
network analysis in which nodes of the communities are tightly connected each
other and loosely connected between other communities. Many algorithms like
Givan-Newman algorithm, modularity maximization, leading eigenvector, walk
trap, etc., are used to detect the communities in the networks. To test the
community division is meaningful we define a quality function called
modularity. Modularity is that the links within a community are higher than the
expected links in those communities. In this paper, we try to give a solution
to the cold-start problem based on community detection algorithm that extracts
the community from the social networks and identifies the similar users on that
network. Hence, within the proposed work several intrinsic details are taken as
a rule of thumb to boost the results higher. Moreover, the simulation
experiment was taken to solve the cold start problem.