Hi: I am working on a proyect where we want to recommend our users products based on their previous 'likes', purchases and so on (typical stuff of a recommender system), while we want to let them browse freely the catalogue by search queries, making use of facets, more-like-this and so on (typical stuff of a Solr index).
After reading here and there, I have reached the conclusion that's it's better to keep Solr Index apart from the database. Solr is for products (which can be reindexed from the DB as a nightly batch) while the DB is for everything else, including -the products and- user profiles. So, given an user and a particular search (which can be as simple as "q=*"), on one hand we have Solr results (i.e. docs + scores) for the query, while on the other we have user predicted ratings (i.e. recommender scores) coming from the DB (though they could be cached elsewhere) for each of the products returned by Solr. And what I want is clear -to state-: combine both scores (e.g. by a simple product) so the user receives a sorted list of relevant products biased by his/her preferences. I have been googleing for the last days without finding which is the best way to achieve this. I think it's not a matter of boosting, or at least I can't see which boosting method could be useful as the boost should be user-based. I think that I need to extend -somewhere- Solr so I can alter the result scores by providing the user ID and connecting to the DB at query time, doing the necessary maths and returning the final score in a -quite- transparent way for the Web app. A less elegant solution could be letting Solr do its work as usual, and then navigate through the XML modifying the scores and reordering the whole list of products (or maybe just the first N results) by the new combined score. What do you think? A big THANKS in advance Álvaro -- View this message in context: http://lucene.472066.n3.nabble.com/Combining-Solr-score-with-customized-user-ratings-for-a-document-tp4040200.html Sent from the Solr - User mailing list archive at Nabble.com.
