4 Responses

  1. eviledv
    eviledv February 28, 2009 at 07:16 |

    Hi Vegard.

    While content-based recommendations is decidedly a good idea, is it really a necessity for getting rid of the Coldplay-problem[TM]?

    It should be trivial (with the caveat that I have considered this for about 5 minutes) to set up a probabilistic model that takes into account the variance in connectivity. So that a co-occurrence of purchases is only considered meaningful if that co-occurrence is significant given the potentially huge sales of one or both of the items. Anyway, maybe the futility of this approach is explained in the thesis, but laziness prevailed over 200+ pages.

    Of course the assumption is that vendors are actually interested in this. The current system might be deliberate as people might be more likely to purchase something they recognize (and that everyone else has) than try something new.

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  2. Vegard Sandvold
    Vegard Sandvold February 28, 2009 at 10:47 |

    Hi Eivind!

    I find it difficult to say how your suggestion would work. I’ll admit to my own limited understanding of social recommenders, and pass the question on to Oscar himself.

    Familiarity matters when it comes to recommendations. It’s true that we’re more likely to purchase something we recognize. But it would be a shame if all we end up doing is skimming the surface of popularity. Popularity should be balanced with novelty.

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  3. eviledv
    eviledv March 4, 2009 at 15:46 |

    Hi again, got drunk and forgot about this :-)

    > I’ll admit to my own limited understanding of social recommenders

    I’m sure you know more than me :-)

    Let’s assume we have purchased record A from The Totally Extreme Niche Band and want recommendations based on that record. Looking at the purchasing connection network we see that everyone who bought A also bought B. However B is from The Red Hot Chili Beatles which absolutely everyone owns anyway so the co-occurrence of these purchases (A and B) doesn’t really mean anything. There is however another band C: The Supreme Niche Tunes that is highly similar to A, but due to it’s low sales figures is largely ignored.

    Now, given this and the overall probabilities of purchases P(A)=low, P(B)=high, P(C)=low (i.e. normalized sales figures) it is possible to calculate how “surprised” one should be at co-occurrences in purchasing patterns. In our example there is likely no dependency between purchasing record A and B (because everyone buys B) so P(A) and P(B) would be independent events and their probability of co-occurrence would be simply P(A)P(B). A and C however, due to their high similarity would be highly dependent (presumably driven by some enthusiasts-savants in the genre) so the probability of their co-occurrence would be much higher than P(A)P(C). The unexpectedness of this dependency can be described in various ways, p-value/mutual information etc. which can be ranked and compared to other dependencies e.g. the dependency of P(A)P(B). The latter would be small and C would consequently be ranked above B when considered as a recommendation linked to A.

    Of course there is no doubt that an some musical similarity measure (if implemented properly) could be superior. The obvious reason being that a purely probabilistic purchasing scheme depends on a certain number of enthusiast making the right purchases in the first place.

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  4. Vegard Sandvold
    Vegard Sandvold March 4, 2009 at 20:40 |

    >> I’ll admit to my own limited understanding of social recommenders

    > I’m sure you know more than me :-)

    Perhaps I do, but you sure know more about combinatorics than I do :-)

    I think you’re describing a system where generally popular items (artists) are penalized for their connectedness (inbound/outbound links). That makes sense, although you lost me with all the P’s. I guess, with my limited understanding of social recommenders, that it is common to implements something similar.

    I really recommened checking out the presentation I’ve embedded into this post. Should be a breeze for a wicked number crusher like yourself.

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