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From Paul Loy <>
Subject Re: Recommendations from flat data
Date Tue, 05 May 2009 12:44:59 GMT
The BooleanUserGenericUserBasedRecommender did not work for me. It still
just gave an unordered list of recommendations with a value of 1. I think my
tally method is good enough for my proof-of-concept at the moment so I'll
roll with that.

Thanks for all the help!


On Tue, May 5, 2009 at 12:46 PM, Sean Owen <> wrote:

> On Tue, May 5, 2009 at 11:56 AM, Paul Loy <> wrote:
> > I made a subclass of MySQLJDBCDataModel and had to copy some of the code
> > from both AbstractJDBCDataModel. I'm thinking there could be a better way
> to
> > do this but would require a bit of a refactor. I might give it a go today
> if
> You should just have to override buildUser() to return a
> BooleanPrefUser instead.
> > I have time. I'd also like to have the queries injected so we could make
> a
> > pure JDBC Model and not require subclasses for each SQL database out
> there.
> > I'll have a look at that too.
> That's what AbstractJDBCDataModel should be about. The constructor
> takes a bunch of SQL queries.
> > it's taking about a minute to get a recommendation. I'm guessing with an
> > index on user_id column it will be even quicker (also, if I'm not
> importing
> > millions of rows in the background).
> Oh my yes, you absolutely need indexes, on the user ID and item ID
> column. The composite primary key should be both of these columns.
> > The only issue I have with recommendations from the
> > BooleanTanimotoCoefficientSimilarity is that there is no way to order the
> > results as they all come out with a value of 1. So the least relevant
> item
> > may come out at the top. So instead of using a recommender, what I do is
> get
> > the items from the 20 nearest neighbours, remove from that list the items
> my
> Yeah you are right, in this scenario a user-based recommender breaks
> down somewhat since all prefs are the same, and recommendations are
> based on weighted preferences, but that always comes out 1!
> Instead try BooleanUserGenericUserBasedRecommender. The 'estimated
> preferences' you get out are bogus in the sense that all prefs really
> *should* be 1, if anything, but, instead you get a value which is the
> sum of similarity to all users who express a pref for that item. It
> should give a desirable ordering. Try it and see what happens.
> The whole 'boolean user' thing is important but I am have trouble
> thinking of how to efficiently piece it into the whole framework...
> it's a lot of copy-and-paste and tortuously long class names now, but,
> should work at least.

Paul Loy

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