  We address the problem of personalization in the context of eCommerce search.
Specifically, we develop personalization ranking features that use in-session
context to augment a generic ranker optimized for conversion and relevance. We
use a combination of latent features learned from item co-clicks in historic
sessions and content-based features that use item title and price.
Personalization in search has been discussed extensively in the existing
literature. The novelty of our work is combining and comparing content-based
and content-agnostic features and showing that they complement each other to
result in a significant improvement of the ranker. Moreover, our technique does
not require an explicit re-ranking step, does not rely on learning user
profiles from long term search behavior, and does not involve complex modeling
of query-item-user features. Our approach captures item co-click propensity
using lightweight item embeddings. We experimentally show that our technique
significantly outperforms a generic ranker in terms of Mean Reciprocal Rank
(MRR). We also provide anecdotal evidence for the semantic similarity captured
by the item embeddings on the eBay search engine.
