Abstract
With the rapid development of e-commerce, there is a huge amount of commodity data on the Internet. Users are always spending a lot of time looking for the exact product. Therefore, finding products representing the same entity is an effective way to improve the efficiency of purchasing. Due to frequently missing or wrong values and subjective difference in description, traditional method of entity resolution may not have a good result on e-commerce data. Therefore, a set of algorithms are proposed in data cleaning, attribute and value tagging, and entity resolution, which are specialized for e-commerce data. In addition, user’s actions are collected to improve the classification result. The chapter evaluates the effectiveness of the proposed algorithms with real-life datasets from e-commerce sites.