The growth of the number of e-commerce users and the items being sold become both opportunities and challenges for e-commerce marketplaces. As the existence of the long-tail phenomenon, the marketplaces need to pay attention to the high number of rarely sold items. The failure to sell these products would be a threat for some B2C e-commerce companies that apply a non-consignment sale system because the products cannot be returned to the manufacturer. Thus, it is important for the marketplace to boost the promotion of long-tail products. The objective of this study is to adapt the graph-based technique to build the recommendation system for long-tail products. The set of products, customers, and categories are represented as nodes in the tripartite graph. The Absorbing Time and Hitting Time algorithms are employed together with the Markov Random Walker to traverse the nodes in the graph. We find that using Absorbing Time achieves better accuracy than the Hitting Time for recommending long-tail products.
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.
Customer satisfaction becomes a key influencer for people's habits or daily activities. One of the examples is in the decision-making process about whether they will use specific products or services. People often need other's review or rating about what they are going to use or consume. In this research, by using customer's online review that available from Airbnb website, we try to extract what are the most talked factors about peer-to-peer accommodation, and how customer sentiment about them. We use Latent Dirichlet Allocation (LDA) to extract that factors and conduct sentiment analysis by utilizing semantic analyzer from Google Cloud NLP. We analyze which factors that has more effect on customer satisfaction, not only in general but more specific based on customer gender and tourism destination object. The result shows that factors related to social benefit and service quality have impact on customer satisfaction, moreover different customer gender and different tourism object destination bring different sentiment among customer. We also find several factors that can be improved by the owner of the accommodation to improve customer satisfaction toward their services.
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