The popularity of social media sites like Twitter and Facebook opens up interesting research opportunities for understanding the interplay of social media and e-commerce. Most research on online behavior, up until recently, has focused mostly on social media behaviors and e-commerce behaviors independently. In our study we choose a particular global e-commerce platform (eBay) and a particular global social media platform (Twitter). We quantify the characteristics of the two individual trends as well as the correlations between them. We provide evidences that about 5% of general eBay query streams show strong positive correlations with the corresponding Twitter mention streams, while the percentage jumps to around 25% for trending eBay query streams. Some categories of eBay queries, such as 'Video Games' and 'Sports', are more likely to have strong correlations. We also discover that eBay trend lags Twitter for correlated pairs and the lag differs across categories. We show evidences that celebrities' popularities on Twitter correlate well with their relevant search and sales on eBay. The correlations and lags provide predictive insights for future applications that might lead to instant merchandising opportunities for both sellers and e-commerce platforms.

User expectation and experience for web search and eCommerce (product) search are quite different. Product descriptions are concise as compared to typical web documents. User expectation is more specific to find the right product. The difference in the publisher and searcher vocabulary (in case of product search the seller and the buyer vocabulary) combined with the fact that there are fewer products to search over than web documents result in observable numbers of searches that return no results (zero recall searches). In this paper we describe a study of zero recall searches. Our study is focused on eCommerce search and uses data from a leading eCommerce site's user click stream logs. There are 3 main contributions of our study: 1) The cause of zero recall searches; 2) A study of user's reaction and recovery from zero recall; 3) A study of differences in behavior of power users versus novice users to zero recall searches.

Query suggestion module is an integral part of every search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in the domain of e-commerce for product search. In this paper, we discuss query suggestion and its methodologies in the context of e-commerce search engines. We show that dynamic inventory combined with long and sparse tail of query distribution poses unique challenges to build a query suggestion method for an e-commerce marketplace. We compare and contrast the design of a query suggestion system for web search engines and e-commerce search engines. Further, we discuss interesting measures to quantify the effectiveness of our query suggestion methodologies. We also describe the learning gained from exposing our query suggestion module to a vibrant community of millions of users.