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Addressing the cold start problem in privacy preserving content-based recommender systems using hypercube graphs
The initial interaction of a user with a recommender system is problematic because, in such a so-called cold start situation, the recommender system has very little information about the user, if any.
A machine learning framework for neighbor generation in metaheuristic search
Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space.
Exploring dual information in distance metric learning for clustering
To address these issues, we propose to exploit the dual information associated with the pairwise constraints of the semi-supervised clustering problem.
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