It's a fairly typical recommendation scenario: a user is looking at an item and gets suggestions for other items based on the number of users that like this item liking those other items also.
So, I'm trying to implement this using Neo4j. Obviously, the graph contains user nodes and item nodes connected by the "likes" relationship. Currently, I'm not aware of any direct way to count in Neo, so I was thinking of implementing this requirement as follows (in pseudo code): *have a hashmap to store count per item (<itemId, count>) *start from the item the user is looking at *traverse "likes" relationships, in both directions, breath-first, 2 levels deep *for every item node encountered, increment the count for that node It is vital for this algorithm to be as fast as possible, so I'm asking if there's any better way to implement it? Also, do you think it would work faster in Neo compared to a traditional RDbMS (with a user-table, item-table and an interconnecting table)? Thanks for any advice :) -- View this message in context: http://neo4j-user-list.438527.n3.nabble.com/Recommendation-based-on-likes-tp992069p992069.html Sent from the Neo4J User List mailing list archive at Nabble.com. _______________________________________________ Neo4j mailing list [email protected] https://lists.neo4j.org/mailman/listinfo/user
