How does Hugo's Related Content algorithm work? What are the factors?

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On their website they say:

Hugo uses a set of factors to identify a page’s related content based on Front Matter parameters. This can be tuned to the desired set of indices and parameters or left to Hugo’s default Related Content configuration.

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But how exactly does the algorithmus work? What are the factors?

recommendation-engine hugo go static algorithm
1 Answers

The original approach is explained in gohugoio/hugo PR 3815

Several attempts have been started to fix #98 -- all of them have failed for some reason.
It is a hard problem to solve, and I think the main reason for failure has been the bottom-up-approach, i.e. we have started with the hardest problem: Solving Sherlock's last case.

The reason I'm picking up this ball again now is this Twitter thread:

Using intersect and keywords in page params work reasonably well, but it is quadratic and will be slow to unusable for larger sites.

So, instead of solving the hardest problem, I have started on this PR by outlining an interface:

type PageSearcher interface {
  Search(args ...interface{}) (Pages, error)
  SearchIndex(index string, args ...interface{}) (Pages, error)
  Similar(p *Page) (Pages, error)
  SimilarIndex(index string, p *Page) (Pages, error)
}

Naming suggestions welcomed.

The idea is that a user defines a set of indexes in config.toml:

indexes:
 - param: keywords
   weight: 1
- param: tags
   weight: 3

Then we lazily build some sort of index from that, and then you can do fast searches like:

{{ .Site.RegularPages.Similar . }}
{{ .Site.RegularPages.Search "hugo" }}
{{ .Site.RegularPages.SearchIndex "keywords" "hugo" | limit 10 }}

Initial implementation: gohugoio/hugo commit 3b4f17b

VonC_63090
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