# Evaluate RecSys
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### Similarity Metrics

**Dot Product:** 
- Note that popular items tend to be recommended for everyone, especially when
  using dot product as a  similarity measure.

**Cosine Similarity**
- Cosine Similarity is a measurement that quantifies the similarity between two
  or more vectors. Range: `[0 - 1]`.

 ![image](../assets/cosine2.png)

- The similarity measurement is a measure of the cosine of the angle between
  the two non-zero vectors A and B.
- As the cosine similarity gets closer to 1, the angle between the two vectors
  [A,B] is smaller.  
- Computes similarity as the normalized dot product of X and Y:

![image](../assets/cosine.png)

**<span class="label label-info">Notes</span>**
- In order to compute similarities, the entire dataset needs to be
loaded into memory. One solution would be to convert from a dense to a sparse
matrix representation given that many of the rows have zero entries. 
- Using implementation sparse.csr_matrix can be used as it's very memory efficient for
storing sparse datasets. 

### Mean Average Precision (MAP)
- MAP to evaluate a recommender algorithm implies that we are treating the
  recommendation like a ranking task.  
- We want to show the top recommendations first and maybe market them more
  aggressively. 

![image](../assets/map.png)

### Coverage
- Is the percent of items in the training data the model is able to recommend on a test set.

### Personalization
- Is a great way to assess if a model recommends many of the same items to different users. 
- We can use the cosine similarity metric to measure the similarity among the
  top “K” items recommended.  
- A high personalization score indicates user’s recommendations are different,
  meaning the model is offering a personalized experience to each user.