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EDBT
2016
ACM

Cluster-based Contextual Recommendations

3 years 18 days ago
Cluster-based Contextual Recommendations
In this work, we address the problem of contextual recommendations by exploiting the concept of fault-tolerant subspace clustering. Specifically, we pre-partition users that have similarly rated subsets of data items into clusters and associate with each cluster a context situation. Context is defined as any internally stored information that can be used to characterize the data per se. Then, given a query context, we identify the clusters with the most similar context, and use their members for making suggestions in a collaborative filtering manner.
Added 02 Apr 2016
Updated 02 Apr 2016
Type Journal
Year 2016
Where EDBT
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