By the way, it's quite a pity that no such dataset is publicly available with feedbacks on recommendations. As a result, researches on this topic is quite limited.
2010/4/28, 补丁象夫 <mahout.c...@gmail.com>: > You have two sources of information, feedbacks on recommendations and > ratings volunteerly provided by the users. Therefore, the model should > balance those two types of information. > > As to how to utilize the feedbacks on recommendations, I think it's a > learning-to-rank task. Recommended items with negative feedbacks > should not be taken as irrelevant items, they are only less relevant > compared with recommendations with positive feedbacks or no feedbacks. > There are many works on learning to rank, and CofiRank by Yahoo is > probably the first work to adopt such method for collaborative > filtering. > > > > > > > 2010/4/28, Sean Owen <sro...@gmail.com>: >> You are talking about feedback on recommendations, right? When a user >> says "that recommendation you presented to me is not a good >> recommendation"? I think this is fairly different information from a >> user volunteering that "I don't like that item." An item that a user >> rates poorly is still, strangely, an item that is relevant to that >> user. >> >> I kind of wrote about this in the book -- let's say you are a >> hard-core classical music fan. You love Brahms (5 stars) but can't >> stand the showy Vivaldi (1 star). If I recommended Led Zeppelin to >> you, you would undoubtedly say it's a bad recommendation. But it is >> 'bad' in a different way that Vivaldi is bad. Vivaldi is something you >> knew about already because it was similar to things you do like -- Led >> Zeppelin was completely foreign and unrelated. >> >> Negative ratings like the 1 star that the user volunteered for >> Vivaldi, therefore, mean some different and should be treated >> differently than the user's response to the Led Zeppelin >> recommendation that was pushed at him or her. >> >> >> I would agree with Ted, that negative feedback on recommendations >> doesn't somehow belong in the data model. You can use it to filter >> recommendations -- you would want to purge any Led Zeppelin records >> from recommendations going forward. In the (non-distributed) >> framework, this is done with a Rescorer. >> >> That is, you don't need to or want to add this negative feedback into >> the model. There's no problem of boolean ratings and such since the >> association would not exist in the model -- just in recommendation >> filters. >> >> >> >> On Tue, Apr 27, 2010 at 6:27 PM, Tolga Oral <tolga.o...@gmail.com> wrote: >>> We are building a system on top of provided recommendations allowing >>> user >>> to >>> provide feedback. I have a question on how to apply the negative >>> feedback >>> for both boolean and none boolean scores. >>> >>> Assuming its a boolean item (item with no score), I cant see how we can >>> provide negative feedback for a given recommendation. Does this mean we >>> should convert our boolean items to a score of 1 and give score of -1 >>> (or >>> 0) >>> for recommendations that user didnt like? >>> >>> This applies to items with score too I am not sure if the correct way of >>> going about this is to provide 0 or a negative value. >>> >>> Thank you. >>> >> > > > -- > http://blog.sina.com.cn/apachemahout > -- http://blog.sina.com.cn/apachemahout
