Recommended Reading

The point of the exercise is to recommend articles that are closest to the reader's preferences. For example, if the reader is particularly interested in hardware and security, it would be a good idea to suggest articles from these categories – or even articles that are in both categories at the same time. The current workflow has already explored the extent to which a certain reader has a preference for each category, and the result is available in the form of a vector (Figure 5).

You can create a very similar vector for each article, where the columns of those categories contain a 1, to which the article is assigned. Category columns without connection to the article, on the other hand, contain a 0. The One-to-Many node makes this possible: The transformation of the article-heading assignments (Table 2) into a representation by a binary vector per article (Figure 10).

Figure 10: Output table of the one-to-many node.

Once the two vector types have been created, the Similarity Search node can simply determine a distance between the vector of a reader (which represents its preferences) and the vector of an article (which indicates to which columns it is assigned). The smaller the distance, the more the reader's preference corresponds to the categories in which the article is classified.

From all articles that a certain reader has not yet read (see Row Reference Filter node), it is now easy to determine the article that has the shortest distance to the reader's preferences. This article is finally recommended for reading.

A loop (consisting of the Chunk Loop Start and Loop End nodes) corresponds to a For loop across all rows of the table. The loop determines the smallest distance of the vectors for each reader. The overall result with one article recommendation per reader (Figure 11) could then be written back to a database with the help of the Database Writer node.

Figure 11: The content of the meta node for article recommendations.

Conclusion

This article only covers a fraction of the nearly 2,000 nodes available for KNIME. Other exciting KNIME features include flow variables, the Workflow Coach, streaming, nodes for processing texts, and Deep Learning capabilities.

You can get an idea of the KNIME Analytics Platform by downloading and testing the software. Check out the documentation [3] and node guide [4] at the KNIME website for more on working with KNIME, or bring your questions to the KNIME Forum [5].