Hi Sean, Thanks. When I expand the neighborsize into 1000, there are 80 items in common when giving 500 recommendations. That's quite reasonable and accepted. -- Young
At 2010-08-30 23:55:15，"Sean Owen" <[email protected]> wrote: >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 >>
