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

Personalized Ranking in eCommerce Search