Product Recommendation in Offline Retail Industry by using Collaborative Filtering

Abstract

The variety of purchased products is important for retailers. When a customer buys a specific product in a large number, the customer might get benefit, such as more discounts. On contrary, this could harm the retailers since only some products are sold quickly. Due to this problem, big retailers try to entice customers to buy many variations of products. For an offline retailer, promoting specific products based on the markets' taste is quite challenging because of the unavailability of information regarding customers' preferences. This study utilized four years of purchase transaction data to implicitly find customers' ratings or feedback towards specific products they have purchased. This study employed two Collaborative Filtering methods in generating product recommendations for customers and find the best method. The result shows that the Memorybased approach (k-NN Algorithm) outperformed the Modelbased (SVD Matrix Factorization). Another finding is that the more data training being used, the better the performance of the recommendation system will result. To cope with the data scalability issue, customer segmentation through k-Means Clustering was applied. The result implies that this is not necessary since it failed to boost up the models' accuracy. The result of the recommendation system is then applied in a suggested business process for a specific offline retailer shop.

Machine learning based recommender system for e-commerce

“…The Pearson correlation is useful when the recommendation system uses explicit information while the cosine measure is used when there is implicit information such as a purchase history [23]. When collaborative filtering is used to create a model, other techniques such as segmentation [24] are used to discover latent information that could explain the purchase of a product or the outcome of a quote.…”

Rekomendasi Produk Berbasis Collaborative Filtering Menggunakan Factorization Machine Graph Convolutional Networks

“…Dalam industri ritel, rekomendasi produk dapat digunakan untuk meningkatkan penjualan melalui peningkatan up-selling dan cross-selling. Misalnya, di sebuah toko pakaian, saat seorang pelanggan memilih sebuah celana jeans, rekomendasi dapat menyarankan atasan yang cocok atau aksesori yang sesuai dengan gaya dan warna celana tersebut [6]. Dalam industri perhotelan, rekomendasi produk dapat membantu meningkatkan pengalaman tamu dan mempersonalisasi layanan.…”

Makine Öğrenimi ile Uzun Kuyruk Ürünler için İyileştirilmiş Sonraki Öğe Önerisi

“…Hedef müşteri tarafından verilen derecelendirmeyi tahmin etmek için Bellek tabanlı ve Model tabanlı İşbirlikçi Filtreleme kullanarak daha iyi bir yaklaşım bulmak için testler gerçekleştirilmiştir. Sonuç olarak, k-NN'li Bellek tabanlı CF'nin SVD aracılığıyla Model tabanlı CF'den daha iyi performans gösterdiği vurgulanmıştır [26].…”