Information Discovery in e-Commerce (2410.05763v3)

Abstract: Electronic commerce, or e-commerce, is the buying and selling of goods and services, or the transmitting of funds or data online. E-commerce platforms come in many kinds, with global players such as Amazon, Airbnb, Alibaba, eBay and platforms targeting specific geographic regions. Information retrieval has a natural role to play in e-commerce, especially in connecting people to goods and services. Information discovery in e-commerce concerns different types of search (e.g., exploratory search vs. lookup tasks), recommender systems, and natural language processing in e-commerce portals. The rise in popularity of e-commerce sites has made research on information discovery in e-commerce an increasingly active research area. This is witnessed by an increase in publications and dedicated workshops in this space. Methods for information discovery in e-commerce largely focus on improving the effectiveness of e-commerce search and recommender systems, on enriching and using knowledge graphs to support e-commerce, and on developing innovative question answering and bot-based solutions that help to connect people to goods and services. In this survey, an overview is given of the fundamental infrastructure, algorithms, and technical solutions for information discovery in e-commerce. The topics covered include user behavior and profiling, search, recommendation, and language technology in e-commerce.

• The paper presents a dual-stage recommendation framework that balances candidate retrieval and ranking for efficiency and accuracy.
• The paper leverages multimodal data from search logs, user clicks, and reviews to model user behavior and enhance personalization.
• The paper forecasts AI advancements like conversational search and multi-modal techniques to drive future e-commerce innovations.

Information Discovery in e-Commerce

The paper "Information Discovery in e-Commerce" provides a comprehensive survey of the methodologies and techniques related to information discovery in e-commerce platforms. This field has gained significant traction due to the increasing importance of e-commerce in contemporary shopping experiences. The research explores various dimensions of information discovery, including search, recommendation, and natural language processing, which are crucial for facilitating seamless user interactions on e-commerce portals.

Key Topics and Contributions

E-commerce Platforms and User Interaction: The authors identify that e-commerce platforms like Amazon, Alibaba, and eBay have transformed shopping habits. These platforms generate extensive multimodal data, including search logs, user clicks, and reviews, which offer rich insights into user preferences.

Information Discovery Challenges: The paper outlines distinct challenges in e-commerce information discovery. These include the vocabulary gap between user queries and product information, the need for personalization in search and recommendations, and handling the massive volume and diversity of data.

Search and Recommendation Strategies: The paper highlights the dual nature of e-commerce search and recommendation tasks. It discusses representation and interaction-based matching techniques for search, enhancing query understanding and product ranking to address the unique needs of e-commerce environments. The two-stage recommendation framework—candidate retrieval followed by candidate ranking—stands as a practical solution to balance computational efficiency and recommendation accuracy.

User Behavior Modelling: The research explores user behavior on e-commerce platforms, focusing on actions such as clicking, adding to carts, and purchasing. Models for predicting these behaviors are essential for understanding user intent and improving recommendations.

Evaluation Metrics: A variety of metrics, including both relevance-based and revenue-based measures, such as NDCG, MRR, and GMV, are discussed to evaluate the effectiveness of search and recommendation systems in driving sales and enhancing user satisfaction.

Future Directions: The paper speculates on future advancements in AI, like conversational e-commerce search, leveraging advanced pre-trained LLMs, and multi-modal search strategies. These avenues promise to enhance user experience by providing more intuitive and context-aware interactions.

Implications and Future Work

The implications of this research are broad, impacting both theoretical and practical aspects of e-commerce platforms:

• Practical Applications: The insights are directly applicable to designing more effective and personalized e-commerce systems, which can result in higher conversion rates and customer satisfaction.
• Theoretical Contributions: The paper provides a structured overview of existing methods and identifies research gaps, guiding future exploration in the integration of machine learning and natural language processing within e-commerce contexts.

In conclusion, the paper lays a robust foundation for future research in e-commerce information discovery, emphasizing the need for innovative approaches to manage and interpret the vast data generated by online shopping activities. It calls for continued exploration into scalable and adaptive models that can comprehend and predict consumer behavior, thereby refining the interaction between users and digital commerce platforms.