Entity resolution identifies entities from different data sources that refer to the same real-world entity. It is an important prerequisite for data cleaning, data integration and data mining, and is a key in ensuring data quality. With the rapid growth of E-commerce, diversity of products and flexible buying patterns of consumers, product identification and matching becomes a long-standing research topic in the big data era. While the traditional entity resolution approaches focus on structured data, the Internet data are neither standardized nor structured. In order to address this problem, this paper presents a synthesized similarity method to calculate similarity between different products. An agglomerate hierarchical clustering method is used to identify products from different sources. Also, the approach is optimized to improve efficiency of execution in three aspects: global cache, knowledge constraints, and blocking strategies. Finally, a series of experiments are performed on real data sets. The experimental results show that the proposed approach has a better performance compared with others.
