Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search

In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products.

Swift Trust in Web Vendors

With the growth of product search engines such as pricegrabber.com, Web vendors have many more casual visitors. This research examines how Web vendors may foster “swift trust” as a means to convert casual visitors to paying customers. We examine whether perceptions of Web sites’ appearance features (normality, social presence and third-party links) and functionality features (security, privacy, effort expectancy and performance expectancy) positively relate to swift trust in a Web vendor. Using a quasi-experimental research design, we empirically test the proposed relationships. Based on an analysis of 224 respondents, we found appearance and functionality features explained 61% of the variance in swift trust. The article concludes with a discussion of findings and implications.

Swift Trust in Web Vendors

With the growth of product search engines such as pricegrabber.com, web vendors have many more casual visitors. This research examines how web vendors may foster “swift trust” as a means to convert casual visitors to paying customers. We examine whether perceptions of website’s appearance features (normality, social presence and third-party links) and functionality features (security, privacy, effort expectancy and performance expectancy) positively relate to swift trust in a web vendor. Using a quasi-experimental research design, we empirically test the proposed relationships. Based on an analysis of 224 respondents, we found appearance and functionality features explained 61% of the variance in swift trust. The paper concludes with a discussion of findings and implications.