I'm reading Yehuda Koren's paper: "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model" SIGKDD 2008.

I notice that in the traditional neighborhood method, say the baseline one:

$\hat{r_{ui}} = \mu + b_u + b_i $

Here the bias of users and items is denoted as two constants:$b_u$ and $b_i$, which could be calculated by averaging all instance of user $u$ and item $i$ respectively.

However, when it comes to the SVD++ model, the author uses $b_{ui}$ instead of $b_u + b_i$, i.e.,

$\hat{r_{ui}} = b_{ui} + q_i^T(p_u + |N(u)|^{-1/2} \sum_{j\in N(u)}y_i )$

I was wondering why uses $b_{ui}$ instead of $b_u + b_i$ ? is that related to the parameter estimation used in matrix factorization-based method?


well, this is a sad story that no one answer this question. However, I eventually figured out the answer.

the $b_{ui}$ in SVD++ is just the baseline value generated from baseline model, which is defined aforementioned: $\hat{r_{ui}} = \mu + b_i + b_u := b_{ui} $

It is very easy to confuse it with the bias of user $u$ to item $i$, at least to me, so this might cause some wrong interpretation of the SVD++ model.

| cite | improve this answer | |
  • $\begingroup$ it think you are wrong $\endgroup$ – Rathma Jun 22 '17 at 7:15

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.