Scoring metric for recommendation system I'm working on a project that involves building a news recommendation system. I've come as far as quantifying user interaction with different articles  on the site into user's affinity towards atopic using a bayesian function. I also have quantified the recent articles using LDA into the proportion an article talk about each topic.
my users topic-affinity for a user x looks like this(target-x):
 user_id  interest-topic-0  interest-topic-1  interest-topic-2  interest-topic-3  interest-topic-4  interest-topic-5  interest-topic-6  interest-topic-7  interest-topic-8  interest-topic-9 
       0            0.0257            0.2956            0.0386            0.0643            0.1285            0.0000               0.0            0.0257            0.0386            0.1671  

My quantified articles looks something like this(vectors-v):
post_id   topic-0   topic-1   topic-2   topic-3   topic-4   topic-5   topic-6  topic-7  topic-8   topic-9
      x  0.055048  0.000000  0.742544  0.032286  0.059630  0.000000  0.000000  0.01173      0.0  0.095441
      y  0.000000  0.051172  0.000000  0.000000  0.158314  0.042632  0.022281  0.00000      0.0  0.720676
      z  0.028615  0.000000  0.020919  0.000000  0.000000  0.018940  0.882862  0.00000      0.0  0.046078

The shape of target will always be (10,)
The shape of vectors will always be (num_articles, 10)
Both vectors do not follow the same distribution.
Now I'm trying to figure out the best way to recommend articles from vectors v, for a target user x, given x and v. I've tried distance similarity functions like cosine similarity to find the distance between vectors. The results are satisfactory but I'm looking for a better function/metric to pick out top n recommendations for a user.
Other ways to measure real-valued vector distances:

Euclidean distance
Manhattan distance
Higher order Minkowski distance
Chebyshev distance

Beyond just trying different distance metrics, you can try re-framing the problem or using A/B testing to see which recommendations are most empirically useful for users.