Title: Recommendation by Collaborative Filtering
Version: 0.3.0
Description: Provides methods and functions to implement a Recommendation System based on Collaborative Filtering Methodology. See Aggarwal (2016) <doi:10.1007/978-3-319-29659-3> for an overview.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
LazyData: true
Imports: methods
NeedsCompilation: no
Packaged: 2024-10-02 11:47:28 UTC; PC
Author: Jessica Kubrusly ORCID iD [aut, cre], Thiago Lima [ctb], Lucas Oliveira [ctb]
Maintainer: Jessica Kubrusly <jessicakubrusly@id.uff.br>
Depends: R (≥ 3.5.0)
Repository: CRAN
Date/Publication: 2024-10-04 16:40:06 UTC

Recommendation by Collaborative Filtering

Description

CF is a class of objects that stores information about a recommendation system. This information includes the consumption or rating of each (user, item) pair in the utility matrix MU, the similarities between each pair of users in the similarity matrix SU, the similarities between each pair of items in the similarity matrix SI, the number of items consumed and/or rated by each user in the vector n_aval_u, the number of users who consumed and/or rated each item in the vector n_aval_i, the average rating value of each user in the vector averages_u, the average rating value received by each item in the vector averages_i, the number of items consumed in common by each pair of users in the matrix Int_U, and the number of users in common for each pair of items in the matrix Int_I. The class contains methods such as addNewUser, addNewEmptyUser, deleteUser, addNewItem, addNewEmptyItem, deleteItem, newRating and deleteRating, which modify the object's structure by altering users, items, or consumption data. The class also includes functions such as kClosestItems, topKUsers, and topKItems, which return items to recommend to a user or users to whom an item should be recommended. An object of the CF class is created using the CFBuilder function.

Fields

MU

The Utility Matrix, a matrix that contains all the users' ratings. The rows comprise users and the columns, items.

SU

The user similarity matrix.

SI

The item similarity matrix

IntU

A symmetric matrix that records the number of users in common who consumed each pair of items.

IntI

A symmetric matrix that records the number of items in common that have been consumed by each pair of users.

averages_u

A vector that contains the averages of users' ratings.

averages_i

A vector that contains the averages of items' ratings.

n_aval_u

A vector that stores the number of items rated by each user.

n_aval_i

A vector that stores the number of users who consumed each item.

datatype

A character that indicates the type of data, which can be either "consumption" or "rating".

Methods

addnewemptyitem(Id_i)

A method that adds a new item that has not yet been consumed by any existing user in the recommendation system. Id_i: a character, the new item ID; To add more than one new user, lists can be used. Id_i: a list of characters;

addnewemptyuser(Id_u)

A method that adds a new user who has not yet consumed any existing items in the recommendation system. Id_u: a character, the new user ID; To add more than one new user, lists can be used. Id_u: a list of characters;

addnewitem(Id_i, Ids_u, r = NULL)

A method that adds a new item that has been consumed by already existing users in the recommendation system. Id_i: a character, the new item ID; Ids_u: a character vector, the IDs of the users who consumed the new item; r: a numeric vector, the ratings given by the users for the new item (only for ratings datatype). To add more than one new item, lists can be used. Id_i: a list of characters; Ids_u: a list of characters vectors; r: list of numeric vectors.

addnewuser(Id_u, Ids_i, r = NULL)

A method that adds a new user who consumed items already existing in the recommendation system. Id_u: a character, the new user ID; Ids_i: a character vector, the IDs of the items consumed by the user; r: a numeric vector, the ratings of the items consumed by the new user (only for ratings datatype). To add more than one new user, lists can be used. Id_u: a list of characters; Ids_i: a list of characters vectors; r: list of numeric vectors.

changerating(Id_u, Id_i, r = NULL)

A method that changes a rating or consumption of a user for an item that has already been rated by them. Id_u: a character, the user ID; Id_i: a character, the item ID; r: a numeric, the rating given by Id_u for Id_i (only for ratings datatype). To change more than one ratings, lists can be used. Id_u: a list of characters; Id_i: a list of characters; r: list of numeric vectors.

deleteitem(Id_i)

A method that deletes an item from the recommendation system. Id_i: a character, the item ID; To delete more than one item, lists can be used. Id_i: a list of characters;

deleterating(Id_u, Id_i)

A method that deletes a existing rating or consumption of a user for an item. Id_u: a character, the user ID; Id_i: a character, the item ID; To deletes more than one ratings, lists can be used. Id_u: a list of characters; Id_i: a list of characters.

deleteuser(Id_u)

A method that deletes an user from the recommendation system. Id_u: a character, the user ID; To delete more than one user, lists can be used. Id_u: a list of characters;

newrating(Id_u, Id_i, r = NULL)

A method that adds a new rating or consumption of an existing user for an existing item that had not yet been rated by them. Id_u: a character, the user ID; Id_i: a character, the item ID; r: a numeric, the rating given by Id_u for Id_i (only for ratings datatype). To add more than one new ratings, lists can be used. Id_u: a list of characters; Id_i: a list of characters; r: list of numeric vectors.

Author(s)

Jessica Kubrusly

References

See Also

CFbuilder

Examples

objectCF_r <- CFbuilder(Data = movies[1:500,], Datatype = "ratings", 
similarity = "pearson")
dim(objectCF_r$MU)
colnames(objectCF_r$MU) #movies Id
rownames(objectCF_r$MU) #users Id
dim(objectCF_r$SU)
dim(objectCF_r$SI)
objectCF_r$averages_u
hist(objectCF_r$averages_u)
objectCF_r$averages_i
hist(objectCF_r$averages_i)
objectCF_r$n_aval_u
summary(objectCF_r$n_aval_u)
barplot(table(objectCF_r$n_aval_u))
objectCF_r$n_aval_i
summary(objectCF_r$n_aval_i)
barplot(table(objectCF_r$n_aval_i))
objectCF_r$addnewuser(Id_u = "newuser1", 
Ids_i = "The Hunger Games: Catching Fire", r = 5)
rownames(objectCF_r$MU) #users Id
objectCF_r$n_aval_u["newuser1"]
objectCF_r$averages_u["newuser1"]
objectCF_r$addnewuser(Id_u = "newuser2",
Ids_i = c("Frozen","Her","Iron Man 3"),r = c(2,4,3))
rownames(objectCF_r$MU) #users Id
objectCF_r$n_aval_u["newuser2"]
objectCF_r$averages_u["newuser2"]
objectCF_r$addnewuser(Id_u = list("newuser3","newuser4"),
Ids_i = list(c("Lincoln","Monsters University","The Lego Movie","Frozen"),
c("The Wolverine","The Lego Movie")),r = list(c(1,4,5,4),c(4,5)))
rownames(objectCF_r$MU) #users Id
objectCF_r$n_aval_u[c("newuser3","newuser4")]
objectCF_r$averages_u[c("newuser3","newuser4")]
objectCF_r$newrating(Id_u = list("newuser1","newuser1","newuser2","newuser4"),
Id_i = list("The Lego Movie","Wreck-It Ralph","Fast & Furious 6",
"12 Years a Slave"),r = list(4,5,4,2))
objectCF_r$n_aval_u[c("newuser1","newuser2","newuser3","newuser4")]
objectCF_r$averages_u[c("newuser1","newuser2","newuser3","newuser4")]
objectCF_r$addnewitem(Id_i = "Oppenheimer",
Ids_u = c("newuser1","newuser2","newuser3","newuser4","1","2","4","6","10",
"11","20","32"),r = c(1,2,3,1,5,4,5,4,1,3,5,4))
colnames(objectCF_r$MU)
objectCF_r$n_aval_i["Oppenheimer"]
objectCF_r$averages_i["Oppenheimer"]
objectCF_c <- CFbuilder(Data = movies[1:500,-3], Datatype = "consumption", 
similarity = "jaccard")
dim(objectCF_c$MU)
colnames(objectCF_c$MU) #movies Id
rownames(objectCF_c$MU) #users Id
dim(objectCF_c$SU)
dim(objectCF_c$SI)
objectCF_c$averages_u
objectCF_c$averages_i
objectCF_c$n_aval_u
summary(objectCF_c$n_aval_u)
barplot(table(objectCF_c$n_aval_u))
objectCF_c$n_aval_i
summary(objectCF_c$n_aval_i)
barplot(table(objectCF_c$n_aval_i))
objectCF_c$addnewuser(Id_u = "newuser1", 
Ids_i = "The Hunger Games: Catching Fire")
rownames(objectCF_c$MU) #users Id
objectCF_c$n_aval_u["newuser1"]
objectCF_c$addnewuser(Id_u = "newuser2",
Ids_i = c("Frozen","Her","Iron Man 3"))
rownames(objectCF_c$MU) #users Id
objectCF_c$n_aval_u["newuser2"]
objectCF_c$addnewuser(Id_u = list("newuser3","newuser4"),Ids_i = list(
c("Lincoln","Monsters University","The Lego Movie","Frozen"),
c("The Wolverine","The Lego Movie")))
rownames(objectCF_c$MU)
objectCF_c$n_aval_u[c("newuser3","newuser4")]
objectCF_c$MU["newuser1","The Lego Movie"]
objectCF_c$newrating(Id_u = list("newuser1","newuser1","newuser2","newuser4"),
Id_i = list("The Lego Movie","Wreck-It Ralph","Fast & Furious 6",
"12 Years a Slave"))
objectCF_c$n_aval_u[c("newuser1","newuser2","newuser3","newuser4")]
objectCF_c$averages_u[c("newuser1","newuser2","newuser3","newuser4")]
objectCF_c$addnewitem(Id_i = "Oppenheimer",
Ids_u = c("newuser1","newuser2","newuser3","newuser4","1","2","4","6","10",
"11","20","32"),r = c(1,2,3,1,5,4,5,4,1,3,5,4))
colnames(objectCF_c$MU)
objectCF_c$n_aval_i["Oppenheimer"]
objectCF_c$averages_i["Oppenheimer"]

The constructor function of the CFilt class.

Description

The constructor function of the CFilt class.

Usage

CFbuilder(Data,Datatype,similarity)

CFbuilder(
Data,
Datatype = ifelse(ncol(Data)==2,"consumption","ratings"),
similarity = ifelse(Datatype == "consumption","jaccard","pearson")
)

Arguments

Data

a dataframe with 2 or 3 columns. The first column indicates the user ID, the second the item ID and the third the rating (only if Datatype = 'rating').

Datatype

a character that indicates the data type: 'rating' or 'consumption'.

similarity

a character that indicates the similarity type. For 'datatype='ratings', 'cossine' or 'person'. For datatype='consumption', 'jaccard'.

Value

a CF class object.

Author(s)

Jessica Kubrusly

References

LINDEN, G.; SMITH, B.; YORK, J. Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, v. 7, n. 1, p. 76-80,2003

See Also

CF-class

Examples

CF1 <- CFbuilder(Data = movies[1:300,], Datatype = "ratings", 
similarity = "pearson") #or
CF1_ <- CFbuilder(Data = movies[1:300,])
CF2 <- CFbuilder(Data = movies[1:300,], Datatype = "ratings", 
similarity = "cosine") #or
CF2_ <- CFbuilder(Data = movies[1:300,], similarity = "cosine")
CF3 <- CFbuilder(Data = movies[1:300,-3], Datatype = "consumption", 
similarity = "jaccard") #or
CF3_ <- CFbuilder(Data = movies[1:300,-3])

Similarity calculation Function

Description

Functions that returns the cosine similarity between two items or users.

Usage

cosine(CF, type, i, j)

Arguments

CF

A CF objec

type

"user" or "item"

i

"user" or "item" Id or index

j

"user" or "item" Id or index#'

Author(s)

Jessica Kubrusly

Examples

objectCF_r <- CFbuilder(Data = movies[1:500,], Datatype = "ratings", 
similarity = "cosine")
cosine(CF=objectCF_r,type = "user",i="1",j="2")
cosine(CF=objectCF_r,type = "item",i="Her",j="Frozen")

Recommendation Functions

Description

Function that provide an estimate of the user's rating for the item.

Usage

estimaterating(
  CF,
  Id_u,
  Id_i,
  type = "user",
  neighbors = ifelse(type == "user", nrow(CF$MU) - 1, ncol(CF$MU) - 1)
)

Arguments

CF

A CF object

Id_u

the user Id

Id_i

the item Id

type

"user" or "item"

neighbors

number of neighbors in the calculation.

Author(s)

Jessica Kubrusly

Examples

objectCF_r <- CFbuilder(Data = movies[1:500,], Datatype = "ratings", 
similarity = "cosine")
estimaterating(CF=objectCF_r,Id_u="35",Id_i="Despicable Me 2")
estimaterating(CF=objectCF_r,Id_u="35",Id_i="Her")

Similarity calculation Function

Description

Functions that returns the Jaccard similarity between two items or users.

Usage

jaccard(CF, type, i, j)

Arguments

CF

A CF objec

type

"user" or "item"

i

"user" or "item" Id or index

j

"user" or "item" Id or index#'

Author(s)

Jessica Kubrusly

Examples

objectCF_r <- CFbuilder(Data = movies[1:500,c(1,2)], Datatype = "consumption", 
similarity = "jaccard")
jaccard(CF=objectCF_r,type = "user",i="1",j="2")
jaccard(CF=objectCF_r,type = "item",i="Her",j="Frozen")

Recommendation Functions

Description

Functions that provide items to be recommended to system users.

Usage

kclosestitems(CF, Id_i, k = 10)

Arguments

CF

A CF objec

Id_i

the item Id

k

an integer

Author(s)

Jessica Kubrusly

Examples

objectCF_r <- CFbuilder(Data = movies[1:500,], Datatype = "ratings", 
similarity = "pearson")
kclosestitems(CF = objectCF_r, Id_i = "The Lego Movie")
kclosestitems(CF = objectCF_r, Id_i = "Lincoln", k=5)

Movie ratings by users

Description

A dataset containing 7276 ratings for 50 movies by 526 users. This database was created by Giglio (2014).

Usage

movies

Format

A data frame with 7276 rows and 3 variables:

Id Users

Users identifier. Numbers 1 to 526.

Id Items

Movies identifier. Movies list:

  1. Iron Man 3

  2. Despicable Me 2

  3. My Mom Is a Character

  4. Fast & Furious 6

  5. The Wolverine

  6. Thor: The Dark World

  7. Hansel & Gretel: Witch Hunters

  8. Wreck-It Ralph

  9. Monsters University

  10. The Hangover Part III

  11. Vai Que Dá Certo

  12. Meu Passado me Condena

  13. We’re So Young

  14. Brazilian Western

  15. O Concurso

  16. Mato sem Cachorro

  17. Cine Holliudy

  18. Odeio o Dia dos Namorados

  19. Argo

  20. Django Unchained

  21. Life of Pi

  22. Lincoln

  23. Zero Dark Thirty

  24. Les Miserables

  25. Silver Linings Playbook

  26. Beasts of the Southern Wild

  27. Amour

  28. A Royal Affair

  29. American Hustle

  30. Capitain Phillips

  31. 12 Years a Slave

  32. Dallas Buyers Club

  33. Gravity

  34. Her

  35. Philomena

  36. The Wolf of Wall Street

  37. The Hunt

  38. Frozen

  39. Till Luck Do Us Part 2

  40. Muita Calma Nessa Hora 2

  41. Paranormal Activity: The Marked Ones

  42. I, Frankenstein,

  43. The Legend of Tarzan

  44. The Book Thief

  45. The Lego Movie, , ,

  46. Walking With Dinosaurs

  47. The Hunger Games: Catching Fire

  48. Blue Is The Warmest Color

  49. Reaching for the Moon

  50. The Hobbit: The Desolation of Smaug

Ratings

Movie ratings by users. The ratings follows the Likert scale: 1 to 5.

Similarity calculation Function

Description

Functions that returns the pearson similarity between two items or users.

Usage

pearson(CF, type, i, j)

Arguments

CF

A CF objec

type

"user" or "item"

i

"user" or "item" Id or index

j

"user" or "item" Id or index#'

Author(s)

Jessica Kubrusly

Examples

objectCF_r <- CFbuilder(Data = movies[1:500,], Datatype = "ratings", 
similarity = "pearson")
pearson(CF=objectCF_r,type = "user",i="2",j="3")
pearson(CF=objectCF_r,type = "item",i="Her",j="Frozen")

Recommendation Function

Description

Functions that provide the top k items to be recommended to the user Id_u.

Usage

topkitems(CF, Id_u, k = 10, type = "user")

Arguments

CF

A CF objec

Id_u

the user Id

k

an integer

type

"user" or "item"

Author(s)

Jessica Kubrusly

Examples

objectCF_r <- CFbuilder(Data = movies[1:500,], Datatype = "ratings", 
similarity = "pearson")
u1 = rownames(objectCF_r$MU)[1]
topkitems(CF=objectCF_r,Id_u = u1)
u2 = rownames(objectCF_r$MU)[2]
topkitems(CF=objectCF_r,Id_u = u2)

Recommendation Function

Description

Functions that provide the top k users to recommend the item Id_i.

Usage

topkusers(CF, Id_i, k = 10, type = "user")

Arguments

CF

A CF objec

Id_i

the item Id

k

an integer

type

"user" or "item"

Author(s)

Jessica Kubrusly

Examples

objectCF_r <- CFbuilder(Data = movies[1:500,], Datatype = "ratings", 
similarity = "pearson")
colnames(objectCF_r$MU)
topkusers(CF = objectCF_r, Id_i = "The Lego Movie")
topkusers(CF = objectCF_r, Id_i = "Her")