As global e-commerce platforms continue to expand, companies are entering new markets where they encounter cold-start challenges due to limited human labels and user behaviors. In this paper, we share our experiences in Coupang to provide a competitive cold-start performance of relevance matching for emerging e-commerce markets. Specifically, we present a Cold-Start Relevance Matching (CSRM) framework, utilizing a multilingual Large Language Model (LLM) to address three challenges: (1) activating cross-lingual transfer learning abilities of LLMs through machine translation tasks; (2) enhancing query understanding and incorporating e-commerce knowledge by retrieval-based query augmentation; (3) mitigating the impact of training label errors through a multi-round self-distillation training strategy. Our experiments demonstrate the effectiveness of CSRM-LLM and the proposed techniques, resulting in successful real-world deployment and significant online gains, with a 45.8% reduction in defect ratio and a 0.866% uplift in session purchase rate.

Service computing promotes a large number of web-delivered services, including web services, APIs and data feeds. Composing data, functionalities and even UI from these web-delivered services into a single web application, usually called service mashup, becomes a popular web development paradigm. The web-delivered services can be modeled as mashup components, while the development of mashup actually yields a set of inter-connected mashup components. The growing popularity of mashup components enriches functionality and user experiences, while the possible connections among components are complex and difficult to mashup developers, who might be non-professional programmers or even end-users, as actions over one component may have potential impacts on another. This paper proposes a novel approach for recommending developers in terms of navigation and completion of mashup components with a large-scale components repository. From data-driven perspective, we model the relationships between mashup components by a generic layered-graph model. Developers are allowed to select some initial components as starting point, while a graph-based algorithm recommends how to navigate to potentially relevant mashup components and complete the relevant mashup application. We experimentally demonstrate the efficiency and effectiveness of our approach for rapid mashup construction.

Web applications have played an important role in mobile computing since they are developed by standard web technology, easy to achieve portability and always up to date. As a widely adopted development fashion, mashup integrates data, computation and UI elements from multiple web-delivered services into a single web application, which is a promising way to develop applications for mobile devices. However, developing mobile web applications by mashup has faced two challenges: on one hand, the growing popularity of web-delivered services, especially those equipped with mobile features like LBS, increases the difficulty of finding proper services and connections between services to create situational applications, on the other hand, mobile computing yields for agile development and fast iteration to meet users everchanging requirements, imposing more burden on developers. This paper presents a graph-based approach to helping mobile developers create mobile web applications. Based on this graph model, algorithms by synthesizing different recommendation patterns are designed to assist mashup completion, such as finding the missing components, connections between them, or potentially relevant options.