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## User-based k-nearest neighbors in rrecsys

Given a target user and her positively rated items, the algorithm will identify the $k$-most similar users of the target user. 

The choice of the $k$ nearest neighbors for the neighborhood formation results in a tradeoff:
a very small $k$ leads to few candidate items that can be recommended because there are not a lot of neighbors to support the predictions. In contrast, a very large $k$ impacts precision as the particularities of user's preferences can be blunted due to the large neighborhood size. In most related works $k$ has been set to be in the range of values from 10 to 100, where the optimum $k$ also depends on data characteristics such as sparsity. 


The similarity is measured based on three algorithms: cosine(simFunct ='cos') and Pearson Correlation(simFunct = 'Pearson').

For the Rating Prediction task, to train a model with this algorithm, it is required to define an additional argument, _neigh_ the neighborhood size.
```{r, eval=FALSE}
data("ml100k")
d <- defineData(ml100k)
e <- evalModel(d, folds = 2)
evalPred(e, "ubknn", simFunct = "Pearson", neigh = 10)
```
For the Item Recommendation task, to provide item recommendations, it is required to define two additional arguments, _positiveThreshold_  the threshold for "positive" ratings, and the _topN_ the number of recommended items.
 
```{r, eval=FALSE}
data("ml100k")
d <- defineData(ml100k)
e <- evalModel(d, folds = 2)
evalRec(e, "ubknn", simFunct = "Pearson", neigh = 10, positiveThreshold = 3, topN = 3)
```


The _neigh_ default value is 10.
The _positiveThreshold_ default value is 3.
The _topN_ default value is 10.



The returned object is of type _UBclass_. 

To get more details about the slots read the reference [manual](https://CRAN.R-project.org/package=rrecsys).

