Learn about data engineering strategies and efficient computation techniques for large-scale data processing.
Authors: Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps C Pre-processing steps Dealing with a dataset with millions of rows and complex types like ”categories” and ”dates” requires special engineering considerations. This section outlines the pre-processing steps required to get the data from Ni et al.
Here, we simply take the original gzipped file and extract a list of categories and item ID . This drastically reduces the file size, so we can do the computations in memory. The next step is preparing the rating data. We start by filtering the dataset to only have users with more than 20 ratings. This reduces the dataset considerably as we saw in Fig. 2. We then left-join the data with the category similarity data described above.
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