Hi, To add some informations to Pat's authoritative response :)
You can use Universal Recommener (UR) for this. With purchase as your primary indicator (http://actionml.com/docs/ur_config) By sending item ID in the query you can do item-item similarity ( http://actionml.com/docs/ur_queries) If you want to do hybrid method : with both user based collaborative filtering and item-item similarity, I think you can do it by boosting (bias > 1) items with similar properties as the item being displayed or for items in the users profile in the query. For this last, you will have to query the event server for events relative to a particular user. In the documentation it is said that they should not be supported by SDK nor used by real application under normal circumstances and they are subject to changes. ( http://predictionio.incubator.apache.org/datacollection/eventapi/#debugging-recipes ) I'm interested if someone have another way to do hybrid method. Regards, Marius 2017-03-23 18:08 GMT+04:00 Vaghawan Ojha <[email protected]>: > Hi, > > I've been trying to deploy a recommendation system using > https://github.com/PredictionIO/template-scala-parallel-universal- > recommendation. > > I've purchase history of user something like this: > user_id, product_id and purchase_date, so I will be using user_id and > product_id to determine the recommendation. I'm not sure if I would be able > to customize the default even parameter. > > Do you have any suggestions like which template would be more suitable for > my problem. I don't have data like rating or view state, I only have data > about user and product they purchased. I need something like item based > similarity as well as user based item similarity. > > Any help would be great > > Thank you > Vaghawan >
