That result is quite possible. For example, with a user-based recommender, the only items that can possibly be recommended are those in the user's neighborhood. If the neighborhood is small, it's possible that only 23 unique items exist among users in that neighborhood. You can never get more recommendations than this.
I don't think this result is "bad" per se, but if you want to try to get more recommendations, you really need more 'dense' data. Or, another algorithm may have different properties that are more desirable to you. Try SlopeOneRecommender. 2010/8/30 Young <[email protected]>: > Hi all, > Based on 1M grouplens data, I tried to use user-based recommender and > item-based recommender to give same user the recommendations. But the results > vary so much. There are 4302 items in dataModel. For user 3 or 8, when > returning 500 recommendeditems, there are only 23 items are in common. > In itembased recommender, I use PearsonCorrelationSimilarity. > In userbased recommender, I use NearestNNeighborhood (size 100), > PearsonCorrelationSimilarity. > Should these results be accepted? Or what should I do to improve this > situation? > > Thank you very much. > > -- Young >
