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Neo4j Recommender Engine (Preference Based)


Neo4j Recommender Engine (Preference Based)

By : user2951679
Date : November 18 2020, 11:13 AM
seems to work fine It will be much faster if you rewrite it as an Neo4j server extension, then you can utilize node.getDegree() which is constant time retrieval of a node's degree.
The core code would look like this, you can simplify it by extracting a function for getting the products per user.
code :
Node user1 = db.findByLabelAndProperty(User,"id",1);
long likes1 = user1.getDegree(LIKES,OUTGOING);
Set<Node> products1 = new HashSet<>(likes1);
for (Relationship rel = user1.getRelationships(LIKES,OUTGOING)) {
   products1.add(rel.getEndNode());
}

Node user2 = db.findByLabelAndProperty(User,"id",2);
long likes2 = user2.getDegree(LIKES,OUTGOING);
Set<Node> products2 = new HashSet<>(likes2);
for (Relationship rel = user2.getRelationships(LIKES,OUTGOING)) {
   products2.add(rel.getEndNode());
}

products1.retainAll(products2);

return products1.size() / (sqrt(likes1) * sqrt(likes2));


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Mahout Recommender: What relative preference values are suitable for a GenericUserBasedRecommender?

Mahout Recommender: What relative preference values are suitable for a GenericUserBasedRecommender?


By : Kamil Parczewski
Date : March 29 2020, 07:55 AM
help you fix your problem The actual range does not matter, not for this implementation. 1-100 is OK, 0-1 is OK, etc. The relative values are all that really matters here.
These values are estimated by a simple (linearly) weighted average. Therefore the response ought to be "linear". It ought to match an intuition that if action X gets a score 2x higher than action Y, then X should be an indicator of twice as much interest in real life.
Mahout recommender - adding content-based similarity to item-based recommender

Mahout recommender - adding content-based similarity to item-based recommender


By : Arda Dura
Date : March 29 2020, 07:55 AM
should help you out Yes it's entirely reasonable to combine them. If both similarities are in [0,1], the most sensible combination is simply their product. This is something you inject using ItemSimilarity, not IDRescorer.
IN Query Google App Engine Datastore Java With Preference of Sorting Based on Number of matching keywords in list

IN Query Google App Engine Datastore Java With Preference of Sorting Based on Number of matching keywords in list


By : Turbo
Date : March 29 2020, 07:55 AM
This might help you There is no way to do this natively in the datastore. The way IN works in the Datastore is that it issues one query for each item in your list, then provides the union of the results (really it is using disjunctive normal form (App Engine source code)).
Instead, you could issues the queries yourself to guarantee the order you want (first query for results that match N tags using an AND filter, then N-1, then N-2, ...).
code :
List<String> searchKeyword = Arrays.asList(q.split(","));

Query query = new Query("Car");
query.setFilter(CompositeFilter.and(Arrays.asList(
    new FilterPredicate("tags", FilterOperation.EQUAL, searchKeyword.get(0)),
    new FilterPredicate("tags", FilterOperation.EQUAL, searchKeyword.get(1)))));

List<Entity> entities = ds.prepare(query).asList(withLimit(10));

query.setFilter(CompositeFilter.or(Arrays.asList(
    new FilterPredicate("tags", FilterOperation.EQUAL, searchKeyword.get(0)),
    new FilterPredicate("tags", FilterOperation.EQUAL, searchKeyword.get(1)))));
entities.addAll(ds.prepare(query).asList(withLimit(10));
Mahout Recommender - questions to setup user preference

Mahout Recommender - questions to setup user preference


By : COLE FLUCHEL
Date : March 29 2020, 07:55 AM
like below fixes the issue I did just that with the new Spark Itemsimilarity implementation about a year ago. You'll need a search engine for the recommendations query because Mahout doesn't have a server. I'd suggest using the new "Universal Recommender" engine template with PredicitonIO. It uses Mahout to calculate the model and Elasticsearch to serve it. https://templates.prediction.io/PredictionIO/template-scala-parallel-universal-recommendation
PreditionIO is a framework of integrated components that provide an event server (for event storage) integration with Hadoop/HDFS, Spark, Hbase, and a REST or SDK API. All you do is install it and get the template as a plugin engine. This will provide pretty advanced recommendations queries with multiple event ingestion, a hybrid content-based method to tune results, and several methods of using popular items for backfill when no other recommendations can be made. It also uses realtime user actions for recommendations.
Mahout Item-based recommendation engine with no preference values

Mahout Item-based recommendation engine with no preference values


By : Chris Nathan
Date : March 29 2020, 07:55 AM
this will help Here is a simple code how you can create an instance of your DataModel that uses GenericBooleanPrefDataModel
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