Yes. This can work, particularly for content tags (like demographics) on users. For content tags on documents, you typically have an extra retrieval. First you recommend tags, then you search for the top recommended tags. Tags will have indicators in this sort of case, just like items would normally. Tags are, of course, not tags in the normal sense, but any content based feature of an item or possibly even combinations of content-based features.
On Thu, Aug 28, 2014 at 11:16 AM, Pat Ferrel <[email protected]> wrote: > When we do cooccurrence recs with a search engine we index: > > itemID, list-of-indicator-items > > Then search on the indicator field with user item history. > > Could we use a similar approach for content-based recs? Imagine a content > site where we have run the text through a pipeline that narrows input to > important tokens (lucene analyzer + LLR with threshold of some kind) Then > this goes into RowSimilarity. > > Input: > docID, list-of-important-terms > > output: > docID, list-of-similar-docs > > Then index the list-of-similar-docs and query with the user doc history. > The idea is to personalize the content based recs rather than just show > "docs like this one"
