Name suggestions are appreciated but this is meant to be about the similarity engine (search engine) recommender.
Recently Lucidworks (the Solr people) announced Fusion, a closed source extension to the Lucidworks offering. It includes a recommender API, which makes it easier to deal with non-text “signals”. What they call signal we have been calling “indicator”. They don’t create indicators—just make them easier to index and query. If you’re following this discussion you may want to have a look at it. https://docs.lucidworks.com/display/fusion/Lucidworks+Fusion+Documentation As more interest builds in this approach it’s time to talk about features and use cases: Features: 1) Multiple user acton collaborative filtering type indicators to use more user behavior than possible with single action recommenders. 2) Realtime reaction to new interactions. Both anonymous users and known users with extremely recent interaction history can get personalized recommendations. 3) blended CF, metadata, and content indicators. Blended at query time so the same model will support a great variety of blends without re-training. 4) ability to use context to affect recommendations 5) addresses the cold-start problem with metadata and content indicators. In other words items with no interaction history can be recommended to completely new users 6) simple recs query API encapsulating and insulating the developer from indicator complexity. Use cases: 1) cooccurrence collaborative filtering recommender, better quality because multiple user actions can be used 2) context affects recs so recs can be specialized based on factors like geolocation of user, mobile vs web, place on site, category being browsed, time of year. 3) user profile data can be used skew recs for things like gender or stated categorical preferences 4) enable cold-start recommendations that gracefully and automatically improve in situations where more data is available. 5) enable item-set recs for shopping carts, wishlists, and other session specific groupings 6) improve recommendations in near realtime as users take new actions recs reflect them. This, for example, enables NRT recs for new users based on their recent views because #1 has allowed views to be used to recommend purchases. 7) the same recommender using the same API can be used anywhere along the spectrum of content-based to collaborative filtering based. The developer can use the recommender in exactly the same way as they back-fill data to gradually improve recs. 8) the content-based flavor of the recommender enables personalized recommendations, not just item similarity-based, even without cooccurrence data. This allows content apps like news to personalize content-based recs even though they never get enough CF data on short-lived articles to make CF recs. You can see why I call it universal but maybe that’s a bit too much hyperbole. Did I mist important use cases or needed features?
