simple_predict {CFF} | R Documentation |

In the predicted items list, items with more scores replace in top of the list.

```
simple_predict(ratings, ratings2, ac)
```

`ratings` |
A rating matrix whose rows are items and columns are users. |

`ratings2` |
A matrix the size of the original user-item matrix in which the active user's empty elements are filled. |

`ac` |
The id of an active user as an integer ( |

Collaborative filtering is a recommender system for predicting the missing ratings that an active user might have given to an item. These ratings have been calculated and accumulate in a vector by this function.

`predictedItems` |
A sorted vector of predicted items based on the scores. |

Farimah Houshmand Nanehkaran

Maintainer: Farimah Houshmand Nanehkaran <hoshmandcomputer@gmail.com>

Song, B., Gao, Y., & Li, X. M. (2020, January). *Research on Collaborative Filtering Recommendation Algorithm Based on Mahout and User Model*. In Journal of Physics: Conference Series, Vol. 1437, no. 1, p. 012095, IOP Publishing.

Ramakrishnan, G., Saicharan, V., Chandrasekaran, K., Rathnamma, M. V., & Ramana, V. V. (2020). *Collaborative Filtering for Book Recommendation System*. In Soft Computing for Problem Solving, pp. 325-338, Springer, Singapore.

```
ratings <- matrix(c( 2, 5, NaN, NaN, NaN, 4,
NaN, NaN, NaN, 1, NaN, 5,
NaN, 4, 5, NaN, 4, NaN,
4, NaN, NaN, 5, NaN, NaN,
5, NaN, 2, NaN, NaN, NaN,
NaN, 1, NaN, 4, 2, NaN),nrow=6,byrow=TRUE)
sim <- simple_similarity(ratings, max_score=5, min_score=1, ac=1)
ratings2 <- Score_replace(ratings, sim_index= sim$sim_index, ac=1)
predictedItems <- simple_predict(ratings, ratings2, ac=1)
```

[Package *CFF* version 1.0 Index]