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Zhong et al. [28] proposed Random Partition Matrix Factorization (RPMF), based on a tree structure constructed by using an efficient random partition technique, which explore low-rank approximation to the current sub-rating matrix at each node.
Random Partition.
4, we propose the Random Partition Factorization Machines (RPFM) model which includes algorithm description and discussion with two state-of-the-art random partition-based models.
The work presented in this paper is closely related to context-aware recommendation and random partition on tree structure.
In random partition, the past landslides are randomly divided into two groups instead of two time periods.
We compared our graph partitioning technique to other techniques, a random partition, Oblivious [3], and HDRF [6].
Similar(14)
2, we provide related works about context-aware and random partition-based models.
We discuss the relationship between the proposed RPFM and other state-of-the-art random partition-based methods.
For the remaining data sets random partitions were generated.
Again, the results in Y-axis are normalized to random partitioning result.
Indeed, the random partitioning algorithm in Cassandra has a direct impact on the data retrieval queries.
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