Webpage Recommender System concerning high dimensional and sparse features
Sen Wu, Min Jiang, Xuedong Gao, Guiying Wei · International Conference on Information Science and Digital Content Technology · 2012
In this paper, we design a Webpage Recommender System which clusters users based on users' browsing history to implement collaborative filtering and prepares webpages that may arouse their interest. In order to solve the problem of high-dimensionality and sparsity in collaborative filtering, the proposed system clusters users using CABOSFV, an efficient algorithm for high-dimensional sparse data clustering of binary attributes. And it uses an automatic webpage classifier to solve problems of cold-start and exhausting of recommendations. When online users send requests to the system, it responses them with those well prepared recommendations.