Predicting Component Utilities for Linear-Weighted Hybrid Recommendation
نویسندگان
چکیده
An effective technique for recommendation in social media and other heterogeneous networks is the weighted hybrid of low-dimensional components (WHyLDR). Recent studies have shown this technique is comparable to other integrative approaches while being considerably more flexible. One key issue for the implementation of a WHyLDR system is the choice of components to generate. Research has shown that the contribution of components based on different network paths varies in unexpected and domaindependent ways. This work examines an information theoretic technique for estimating component performance. Using a real-world social media dataset, we show that this technique is useful both for optimization (estimating component weights) and for determining which components to include in a hybrid.
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