I have used thumbs-down-like interactions as like an anti-click, and subtracts from the interaction between the user and item. The negative scores can be naturally applied in a matrix-factorization-like model like ALS, but that's not the situation here.
Others probably have better first-hand experience here, but yes I have heard of recommending to the negative actions as well and ranking results by the difference between the positive and negative predicted rating. That is, subtract out the scores from the negative recs. Filtering is a crude but more efficient version of this. On Thu, Aug 14, 2014 at 6:22 PM, Pat Ferrel <p...@occamsmachete.com> wrote: > Now that we have multi-action/cross-cooccurrences in ItemSimilarity we can > start playing with taking in multiple actions to recommend one. On the demo > site I have data for thumbs up and down but have only been using thumbs up as > the primary action. I then filter recs by a user’s thumbs down interactions. > However there are now some new options. > > 1) Might it be better to use the thumbs down as a second action type? > Basically this would imply that a user’s dislike of certain items may be an > indicator of their liking others? Since we are using Solr to return recs we’d > just use a two field query so no need to combine recs. > > 2) Get completely independent thumbs-down recs and filter by those instead of > only the thumbs-down interactions? Probably a pretty tight threshold or > number of items recommended would be good here to protect against false > negatives. > > The data is there and the demo site is pretty easy to experiment with. I’m > integrating spark-itemsimilarity now so if anyone has a good idea of how to > better use the data, speak up. It seems like 1 and 2 could be used together > so I’ll probably create some setting that allows a user to experiment on > their own recs.
