Electronic Commerce Product Recommendation using Enhanced Conjoint Analysis

While finding any product, there are many identical products sold in the marketplace, so buyers usually compare the items according to the desired preferences, for example, price, seller reputation, product reviews, and shipping cost. From each preference, buyers count subjectively to make a final decision on which product is should be bought. With hundreds of thousands of products to be compared, the buyer may not get the product that meets his preferences. To that end, we proposed the Enhanced Conjoint Analysis method. Conjoint Analysis is a common method to draw marketing strategy from a product or analyze important factors of a product. From its feature, this method also can be used to analyze important factors from a product in the marketplace based on price. We convert importance factor percentage as a coefficient to calculate weight from every attributes and summarize it. To evaluate this method, we compared the ECA method to another prediction algorithm: generalized linear model (GLM), decision tree (DT), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). Our experimental results, ECA running time is 6.146s, GLM (5.537s), DT (1s), RF (10,119s), GBT (45.881s), and SVM (11.583s). With this result, our proposed method can be used to create recommendations besides the neural network or machine learning approach.