Commonsense knowledge used by humans while doing online shopping is valuable but difficult to be captured by existing systems running on e-commerce platforms. While construction of common- sense knowledge graphs in e-commerce is non-trivial, representation learning upon such graphs poses unique challenge compared to well-studied open-domain knowledge graphs (e.g., Freebase). By leveraging the commonsense knowledge and representation techniques, various applications in e-commerce can be benefited. Based on AliCoCo, the large-scale e-commerce concept net assisting a series of core businesses in Alibaba, we further enrich it with more commonsense relations and present AliCoCo2, the first commonsense knowledge graph constructed for e-commerce use. We propose a multi-task encoder-decoder framework to provide effective representations for nodes and edges from AliCoCo2. To explore the possibility of improving e-commerce businesses with commonsense knowledge, we apply newly mined commonsense relations and learned embeddings to e-commerce search engine and recommendation system in different ways. Experimental results demonstrate that our proposed representation learning method achieves state-of-the-art performance on the task of knowledge graph completion (KGC), and applications on search and recommendation indicate great potential value of the construction and use of commonsense knowledge graph in e-commerce. Besides, we propose an e-commerce QA task with a new benchmark during the construction of AliCoCo2, for testing machine common sense in e-commerce, which can benefit research community in exploring commonsense reasoning.