cosDist: Cosine-Based Analysis
In Pooja-Rajkumar/rectools: Advanced Recommender System

Description Usage Arguments Details Author(s) Examples

Similarity-based analysis via inner products of user ratings, and possibly other variables. EXPERIMENTAL for now.

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formUserData(ratingsIn, usrCovs = NULL, itmCats = NULL, fileOut = "") 
cosDist(x, y, wtcovs, wtcats)
predict.usrData(origData, newData, newItem, k, wtcovs = NULL, wtcats = NULL)

`ratingsIn`	Input data frame, training set. Within-row format is (UserID, ItemID, rating).
`usrCovs`	Data frame of user covariates, e.g. gender and age, one row per user.
`itmCats`	Data frame of item categories, e.g. gender and age, one row per user.
`x`	Object of class `usrDatum`.
`y`	Object of class `usrDatum`.
`wtcovs`	Weight to be placed on covariates, relative to ratings variables.
`wtcats`	Weight to be placed on categories, relative to ratings variables.
`origData`	Object of class `'usrData'`, serving as the training set.
`newData`	Object of class `'usrDatum'`, to be predicted.
`newItem`	The item ID of the rating to be predicted.
`k`	Number of nearest neighbors.
`fileOut`	A file name.

The function formUserData inputs the usual (user ID,item ID,rating) data, and outputs an R list, of class usrData that has one element per user ID. That element, of class usrDatum, has the following components:

userID: User ID.
itms: Vector of IDs for items rated by this user.
ratings: Vector of ratings for those items.
usrCovs: Vector of values of covariates, e.g. gender and age, for this user.
itmCats: Vector of proportions for the item categories (need not sum to 1) for this user. The j-th one is the proportion of items rating by this user in item category j.

In cosDist, the "distance" (not actually a metric) between numeric vectors u and v is defined to be (u,v) / sqrt((u,u) (v,v)), where ( , ) means inner product. The function cosDist finds this for two objects of class 'usrDatum', with the inner product being taken on the ratings contained in each of these objects, as well as the covariates and category data if any.

The function predict.usrData uses a training set, of class usrData, to predict one new data point, of class usrDatum.

Norm Matloff and Vishal Chakraborty

ivl <- InstEval 
ivl$s <- as.numeric(ivl$s) 
ivl$d <- as.numeric(ivl$d) 
ivl <- ivl[,c(1,2,7)] 
ivl10 <- ivl[1:10,] 
ivl10ud <- formUserData(ivl10)
ivl10ud[[1]]
# output of last is
# $userID  
# [1] 1  
#   
# $itms  
# [1]  525  560  832 1068  
#   
# $ratings  
# [1] 5 2 5 3  
#   
# attr(,"class")  
# [1] "usrDatum"