Hi everyone, Please join us for our next DM-Meeting. ****DM-Meeting Fall 2017**** WHO: Bijaya Adhikari WHEN: Thursday, Sep. 28 2017, @ 11:00 am WHERE: Torg 3160 A WHAT: Bijaya will talk about his internship project: Title: Mining E-Commerce Query Relations using Customer Interaction Networks Abstract: Customer Interaction Networks (CINs) are a natural framework for representing and mining customer interactions with E-Commerce search engines. Customer interactions begin with the submission of a query formulated based on an initial product intent, followed by a sequence of product engagement and query reformulation actions. Engagement with a product (eg. clicks), signals its relevance to the customer’s product intent. Reformulation to a new query indicates either dissatisfaction with current results, or an evolution in the customer’s product intent. Analyzing such interactions within and across sessions, enables us to discover various query-query and query-product relationships. In this work, we begin by studying the properties of a real-world customer interaction network developed usingWalmart.com’s product search logs. We observe that CINs exhibit significantly different properties compared to other real world networks (eg. WWW, social networks etc.), making it possible to mine intent relationships between queries, based purely on their structural information. In particular, we show that one can formulate the problem of clustering queries with similar intents, as a community detection task on CINs. Our results show that existing community detection methods already do a good job at identifying intent based query clusters, without using any textual features. We further identify their limitations and propose improved methods for the task. Finally, we show how these relations can be exploited to a) significantly improve search quality for poorly performing queries, and b) identify the most influential (aka. Critical) queries whose search quality is crucial in enabling an E-Commerce search engine satisfy the most customers. Via extensive experiments, we show that our CIN based methods significantly outperform existing baselines in practice. Best regards, Sorour