Modeling user multiple interests by an improved GCS approach

User interest profile is the crucial component of most personalized recommender systems. The diversity and time-dependent evolving nature of user interests are creating difficulties in constructing and maintaining a sound user profile. This paper presents a simple but effective model, by using improved growing cell structures (GCS), to address this problem. The GCS is a kind of self-organizing map neural network with changeable network structure. By virtue of the clustering and structure adaptation capability of GCS, the proposed model maps the problem of learning and keeping track of user interests into a clustering and cluster-maintaining problem. Each cluster found by GCS represents an interest category of a user and the cluster maintaining, including cluster addition and deletion, corresponds to the addition of user's new interests and the removal of user's old interests. The proposed model has been validated by a set of experiments performed on a benchmark dataset. Results from experiments show that our model provides reasonable performance and high adaptability for learning user multiple interests and their changes.

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