This paper describes our participation in the product search task of the CLEF 2015 LL4IR Lab. Working within a generative language modeling framework, we represent products as semi-structured documents. Our focus is on establishing a probabilistic mapping from query terms to document fields. We present and experimentally compare three alternatives. Our results show that term-specific mapping is beneficial. We also find evidence suggesting that estimating field mapping priors based on historical clicks outperforms the setting where the priors are uniformly distributed.