  Personalized recommendation benefits users in accessing contents of interests
effectively. Current research on recommender systems mostly focuses on matching
users with proper items based on user interests. However, significant efforts
are missing to understand how the recommendations influence user preferences
and behaviors, e.g., if and how recommendations result in \textit{echo
chambers}. Extensive efforts have been made in examining the phenomenon in
online media and social network systems. Meanwhile, there are growing concerns
that recommender systems might lead to the self-reinforcing of user's interests
due to narrowed exposure of items, which may be the potential cause of echo
chamber. In this paper, we aim to analyze the echo chamber phenomenon in
Alibaba Taobao -- one of the largest e-commerce platforms in the world. Echo
chamber means the effect of user interests being reinforced through repeated
exposure to similar contents. Based on the definition, we examine the presence
of echo chamber in two steps. First, we explore whether user interests have
been reinforced. Second, we check whether the reinforcement results from the
exposure of similar contents. Our evaluations are enhanced with robust metrics,
including cluster validity and statistical significance. Experiments are
performed on extensive collections of real-world data consisting of user
clicks, purchases, and browse logs from Alibaba Taobao. Evidence suggests the
tendency of echo chamber in user click behaviors, while it is relatively
mitigated in user purchase behaviors. Insights from the results guide the
refinement of recommendation algorithms in real-world e-commerce systems.
