Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Using rich social media information for music recommendation via hypergraph model
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
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Co-clustering on heterogeneous data has attracted more and more attention in web mining and information retrieval. The clustering approaches for two type heterogeneous data (bi-type co-clustering) have been well studied in the lit- erature. However, the work on data with more than two types (high-order co-clustering or multi-type co-clustering) is still limited. In this paper, we present a multi-type co- clustering algorithm, which clusters the data from differ- ent types simultaneously. We use a higher-order tensor to model the high-order relationships, each element of which describes the relation (similarity) among a given set com- posed by data objects from every types. Based on the high- order relationships, we embed the multi-type data objects into the low dimensional spaces by the algorithm based on Clique Expansion which can be viewed as a high-order extension of the normalized cut approach. At last, the k- means method is used to cluster the lower dimensional data. Experiment results show the effectiveness of the proposed method on both toy problem and real data.