where,
Np is the total number of predictions,
pui is the predicted rating that a user
u will select an item
i and
rui is the real rating.
Precision. Precision can be defined as the fraction of correct recommendations or predictions (known as True Positive) to the total number of recommendations provided, which can be as represented as follows:
It is also defined as the ratio of the number of relevant recommended items to the number of recommended items expressed as percentages.
Recall. Recall can be defined as the fraction of correct recommendations or predictions (known as True Positive) to the total number of correct relevant recommendations provided, which can be as represented as follows:
It is also defined as the ratio of the number of relevant recommended items to the total number of relevant items expressed as percentages.
F1 Score. F1 score is an indicator of the accuracy of the model and ranges from 0 to 1, where a value close to 1 represents higher recommendation or prediction accuracy. It represents precision and recall as a single metric and can be as represented as follows:
Coverage. Coverage is used to measure the percentage of items which are recommended by the algorithm among all of the items.
Accuracy. Accuracy can be defined as the ratio of the number of total correct recommendations to the total recommendations provided, which can be as represented as follows:
Intersection over union (IoU). It represents the accuracy of an object detector used on a specific dataset
[62][70].
ROC. ROC curve is used to conduct a comprehensive assessment of the algorithm’s performance
[57].
AUC. AUC measures the performance of recommendation and its baselines as well as the quality of the ranking based on pairwise comparisons
[5].
Rank aware top-N metrics. The rank aware top-N recommendation metric finds some of the interesting and unknown items that are presumed to be most attractive to a user
[63][71]. Mean reciprocal rank (MRR), mean average precision (MAP) and normalized discounted cumulative gain (NDCG) are three most popular rank aware metrics.
MRR. MRR is calculated as a mean of the reciprocal of the position or rank of first relevant recommendation
[64][65][72,73]. MRR as mentioned by
[64][65][72,73] can be expressed as follows:
where
u,
Nu and
Ru indicate specific user, total number of users and the set of items rated by the user, respectively.
L indicates list of ranking length (
n) for user (
u) and
k represents the position of the item found in the he lists
L.
MAP: MAP is calculated by determining the mean of average precision at the points where relevant products or items are found. MAP as mentioned by
[65][73] can be expressed as follows.
where P
u represents precision in selecting relevant item for the user.
NDCG: NDCG is calculated by determining the graded relevance and positional information of the recommended items, which can be expressed as follows
[65][73].
where
D (
k) is a discounting function,
G (
u,
n,
k) is the gain obtained recommending an item found at
k-th position from the list L and
G* (
u,
n,
k) is the gain related to
k-th item in the ideal ranking of
n size for u user.
5. Fashion Recommendation System (FRS), Algorithmic Models and Filtering Techniques
FRS can be defined as a means of feature matching between fashion products and users or consumers under specific matching criteria. Different research addressed apparel attributes such as the formulation of colors, clothing shapes, outfit or styles, patterns or prints and fabric structures or textures
[10][58][66][67][10,58,74,75]. Guan et al. studied these features using image recognition, product attribute extraction and feature encoding. Researchers have also considered user features such as facial features, body shapes, personal choice or preference, locations and wearing occasions in predicting users’ fashion interests
[31][67][68][69][70][31,75,76,77,78]. A well-defined user profile can differentiate a more personalized or customized recommendation system from a conventional system
[28][71][28,79]. Various research projects on apparel recommendation systems with personalized styling guideline and intelligent recommendation engines have been conducted based on similarity recommendation and expert advisor recommendation systems
[10][58][72][10,58,61]. Image processing, image parsing, sensory engineering, computational algorithms, and computer vision techniques have been extensively employed to support these systems
[32][73][74][75][76][77][32,80,81,82,83,84].