I'm thinking to use Amazon Mechanical Turk (mturk.com) for cold-start problem, that is, to hire some cheap mturk workers to generate some initial ratings. Does anyone have experience on this?
Daniel On Oct 26, 2011, at 5:15 AM, Sean Owen wrote: > 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. >>
