  The process of liquidity provision in financial markets can result in
prolonged exposure to illiquid instruments for market makers. In this case,
where a proprietary position is not desired, pro-actively targeting the right
client who is likely to be interested can be an effective means to offset this
position, rather than relying on commensurate interest arising through natural
demand. In this paper, we consider the inference of a client profile for the
purpose of corporate bond recommendation, based on typical recorded information
available to the market maker. Given a historical record of corporate bond
transactions and bond meta-data, we use a topic-modelling analogy to develop a
probabilistic technique for compiling a curated list of client recommendations
for a particular bond that needs to be traded, ranked by probability of
interest. We show that a model based on Latent Dirichlet Allocation offers
promising performance to deliver relevant recommendations for sales traders.
