In-Session Personalization for Talent Search
  Previous efforts in recommendation of candidates for talent search followed
the general pattern of receiving an initial search criteria and generating a
set of candidates utilizing a pre-trained model. Traditionally, the generated
recommendations are final, that is, the list of potential candidates is not
modified unless the user explicitly changes his/her search criteria. In this
paper, we are proposing a candidate recommendation model which takes into
account the immediate feedback of the user, and updates the candidate
recommendations at each step. This setting also allows for very uninformative
initial search queries, since we pinpoint the user's intent due to the feedback
during the search session. To achieve our goal, we employ an intent clustering
method based on topic modeling which separates the candidate space into
meaningful, possibly overlapping, subsets (which we call intent clusters) for
each position. On top of the candidate segments, we apply a multi-armed bandit
approach to choose which intent cluster is more appropriate for the current
session. We also present an online learning scheme which updates the intent
clusters within the session, due to user feedback, to achieve further
personalization. Our offline experiments as well as the results from the online
deployment of our solution demonstrate the benefits of our proposed
methodology.
