I suggested that you write your own ItemSimilarity implementation, that can be based on anything you want. That is the part that is mostly up to you.
You'd have to say what your items are, and what their attributes are, to get ideas about how to define a similarity metric based on attributes. Are there tags or categories for the items, for example? if so you could write a similarity metric that uses overlap in category or tag. On Wed, Oct 26, 2011 at 6:38 AM, mrkahvi <[email protected]> wrote: > Dear Mahout Team, > I'm new to Mahout... > Most of explanations about using Mahout i've found are discussing how to > make recommendation using CF. > > Here I wish to create a recommender system using Mahout that makes use of an > item ID to decide which user IDs would be relevant to the item. The item > would be recommended as soon as it is available in the database. But using > CF becomes a problem since in this case, a new item has no sufficien info, > like ratings, buys, and so on. > Sean Owen hinted me to construct Item-Similarity based on attribute, not > ratings. I see.. But i 'm still confused how to do so in Mahout, since > ItemSimilarity is usually constructed by passing DataModel object that is > based on item ratings (user_id, item_id, rating, and timestamp). > He also suggested me to ask here, so i hope anybody of you can help me to > solve this problem. Thanks before.. > > -- > View this message in context: > http://lucene.472066.n3.nabble.com/cold-start-and-attribute-based-ItemSimilarity-implementation-tp3453699p3453699.html > Sent from the Mahout User List mailing list archive at Nabble.com. >
