bajigur-cloth

Rekomendasi produk menggunakan algoritma Clustering User Data and K-Means Clustering

Teknologi python yang digunakan:

Django
Pandas
SciPy
Scikit-learn.

Algoritma rekomendasi produk:

src/products/suggestions.py

from django.contrib.auth.models import User
from sklearn.cluster import KMeans
from scipy.sparse import dok_matrix, csr_matrix
import numpy as np

from .models import Product, Review, Cluster


# Clustering User Data and K-Means Clustering Algorithms
def update_clusters():
    num_reviews = Review.objects.count()

    update_step = 6

    if num_reviews % update_step == 0:
        all_user_names = list(map(lambda x: x.username, User.objects.only('username')))
        all_product_ids = set(map(lambda x: x.product.id, Review.objects.only('product')))
        num_users = len(all_user_names)
        ratings_m = dok_matrix((num_users, max(all_product_ids) + 1), dtype=np.float32)
        for i in range(num_users):
            user_reviews = Review.objects.filter(user_name=all_user_names[i])
            for user_review in user_reviews:
                ratings_m[i, user_review.product.id] = user_review.rating

            k = int(num_users / 10) + 2
            kmeans = KMeans(n_clusters=k)
            clustering = kmeans.fit(ratings_m.tocsr())

            Cluster.objects.all().delete()
            new_clusters = {i: Cluster(name=i) for i in range(k)}
            for cluster in new_clusters.values():
                cluster.save()
            for i, cluster_label in enumerate(clustering.labels_):
                new_clusters[cluster_label].users.add(User.objects.get(username=all_user_names[i]))

            print(new_clusters)

src/products/views.py

@login_required
def add_review(request, product_id):
    product = get_object_or_404(Product, pk=product_id)
    form = ReviewForm(request.POST)
    if form.is_valid():
        rating = form.cleaned_data['rating']
        comment = form.cleaned_data['comment']
        user_name = request.user.username
        review = Review()
        review.product = product
        review.user_name = user_name
        review.rating = rating
        review.comment = comment
        review.pub_date = datetime.datetime.now()
        review.save()
        update_clusters()
        print(update_clusters)

        return HttpResponseRedirect(reverse('product-detail', args=(product.id, )))
    return render(request, 'products/product_detail.html', {'product': product, 'form': form})

@login_required
def user_recommendation_list(request):
    user_reviews = Review.objects.filter(user_name=request.user.username).prefetch_related('product')

    try:
        user_cluster_name = \
            User.objects.get(username=request.user.username).cluster_set.first().name
    except Exception:
        update_clusters()
        time.sleep(1)
        user_cluster_name = \
            User.objects.get(username=request.user.username).cluster_set.first().name

    user_cluster_other_members = \
        Cluster.objects.get(name=user_cluster_name).users \
        .exclude(username=request.user.username).all()
    other_members_usernames = set(map(lambda x: x.username, user_cluster_other_members))

    other_users_reviews = \
        Review.objects.filter(user_name__in=other_members_usernames) \
        .exclude(product__id__in=user_reviews)

    other_users_reviews_product_ids = set(map(lambda x: x.product.id, other_users_reviews))

    product_list = sorted(
        list(Product.objects.filter(id__in=other_users_reviews_product_ids)),
        key=lambda x: x.average_rating(),
        reverse=True
    )

    return render(
        request,
        'products/user_recommendation_list.html',
        {'username': request.user.username, 'products': product_list}
    )

Screenshot:

All product
Recommendations product for user
Latest reviews
Recent review & add review some product

How to install & run:

git clone https://github.com/purwowd/bajigur-cloth.git
cd bajigur-cloth/src/
pip3 install -r requirements.txt
python3 manage.py runserver

- Buka browser dan akses alamat: `localhost:8000`

Superuser:

username: admin123
password: admin123

Account demo:

username: test1
password: makanayam