There is confusion in the literature. There are at least two takes.
Categorical distribution
Often, the Dirichlet-multinomial is actually not a compound Dirichlet and a multinomial, but a compound Dirichlet and categorical distribution:
$$p(z|\theta) = \prod_i \theta_i^{z_i}$$
This means that this is about only one categorical variable, not a set. The notation of above would for dice assign the vector $[1,0,0,0,0,0]$ to the face with one pip, $[0,1,0,0,0,0]$ to the face with two pips, etc. Naturally, this means that $\sum_i z_i = 1$.
This gets rid off the $\frac{n!}{\prod_i z_i!}$ factor and leads to the much shorter:
$$p(z|\theta)p(\theta|\alpha) = \frac{1}{B(\alpha)}\prod_i \theta_i^{z_i+\alpha_i-1}$$
To subsequently derive at the Dirichlet-multinomial, you'll have to integrate over:
$$ \int p(z|\theta) p(\theta|\alpha) d\theta = \frac{1}{B(\alpha)} \int \prod_i \theta_i^{z_i+\alpha_i-1} d\theta$$
Now, the Dirichlet didn't come from nowhere... The factor $B(\alpha)$ is a normalization factor:
$$p(\theta|\alpha) = \frac{1}{B(\alpha)} \prod_i \theta_i^{\alpha_i-1} = \frac{\prod_i \theta_i^{\alpha_i-1}}{\int_{\Delta^n} \prod_i \theta_i^{\alpha_i-1} d\theta}$$
with $\int_{\Delta^n}$ corresponding to the condition $\sum_i \theta_i = 1$. (Feel free to improve my notation.)
In other words, the multivariate Beta function is actually this integral directly from the definition:
$$\int_{\Delta^n} \prod_i \theta_i^{\alpha_i-1} d\theta = B(\alpha)$$
And hence the integral:
$$\int \prod_i \theta_i^{z_i+\alpha_i-1} d\theta = B(\alpha+z)$$
Hence:
$$ \int p(z|\theta) p(\theta|\alpha) d\theta = \frac{B(\alpha+z)}{B(\alpha)}$$
Or to end up with something similar looking to the Wikipedia definition:
$$ \int p(z|\theta) p(\theta|\alpha) d\theta = \frac{\prod_i \Gamma(\alpha_i+z_i)}{\Gamma(\sum_i (\alpha_i + z_i))} \frac{\Gamma(\sum_i \alpha_i)}{\prod_i \Gamma(\alpha_i)}$$
Collecting terms:
$$ \int p(z|\theta) p(\theta|\alpha) d\theta = \frac{\Gamma(\sum_i \alpha_i)}{\Gamma(\sum_i \alpha_i + \sum_i z_i)} \prod_i \frac{\Gamma(\alpha_i + z_i)}{\Gamma(\alpha_i)}$$
Note, however that we run $i$ here over the entries in our categorical variable $z$ represented as a vector! This is very different from the Wikipedia definition!
Multinomial distribution
In case of an actual multinomial distribution, counts of $z$ - let's write them $n(z)$ - are actually the topic of consideration, not $z$ itself.
$$p(z|\theta) = \frac{(\sum_k n(z_k))!}{\prod_k (n(z_k)!)} \prod_k \theta_k^{n(z_k)}$$
We now run over $k$ unique variables, not over a vectorized categorical variable.
Of course, we can now again multiply with a Dirichlet distribution and the derivation is along the lines as described before. The result:
$$ \int p(z|\theta) p(\theta|\alpha) d\theta = \frac{(\sum_k n(z_k))!}{\prod_k (n(z_k)!)} \frac{\Gamma(\sum_k \alpha_k)}{\Gamma(\sum_k \alpha_k + \sum_k n(z_k))} \prod_k \frac{\Gamma(\alpha_k + n(z_k))}{\Gamma(\alpha_k)}$$
The term $\sum_k n(z_k)$ can be simplified to $N$. This is the actual Dirichlet-multinomial. It's not pretty, but this is what it is.
N categorical distributions
The third option, and this is meant at the Wikipedia page is the distribution of a sequence of categorical variables. Recall that the multinomial assigns probabilities to the number of extract balls (in an experiment getting n balls out of a bag with k ball types). A sequence of categorical variables assigns a probability to a sequence and has a form without the normalization factor:
$$p(z|\theta) = \prod_k \theta_k^{z_k}$$
Here $k$ runs over the categories. We can now follow the derivation as with the single categorical variable.
$$ \int p(z|\theta) p(\theta|\alpha) d\theta = \frac{\Gamma(\sum_k \alpha_k)}{\Gamma(\sum_k \alpha_k + \sum_k n(z_i=k))} \prod_k \frac{\Gamma(\alpha_k + n(z_i=k))}{\Gamma(\alpha_k)}$$
Note for example that we have $\alpha_k$; the parameter $\alpha$ is now considered the same for each cluster $k$.
Best Answer
The posterior distribution is a distribution in $\theta$ with parameters $x_i + \alpha_i$. This amounts to compute the mean of a particular Dirichlet distribution. This is done here. Therefore: $E_{\pi(\theta|x)} \theta_j = \frac{x_j + \alpha_j}{\sum (x_i+\alpha_i)} = \frac{x_j + \alpha_j}{n + \sum \alpha_i}$.