This paper proposes an approach to applying standardized entity data to improve job search quality and to make search results more personalized. Specifically, we explore three types of entity-aware features and incorporate them into the job search ranking function. The first is query-job matching features which extract and standardize entities mentioned in queries and documents, then semantically match them based on these entities. The second type, searcher-job expertise homophily, aims to capture the fact that job searchers tend to be interested in the jobs requiring similar expertise as theirs. To measure the similarity, we use standardized skills in job descriptions and searchers’ profiles as well as skills that we infer searchers might have but not explicitly list in their profiles. Third, we propose a concept of entity-faceted historical click-through-rates (CTRs) to capture job document quality. Faceting jobs by their standardized companies, titles, locations, etc., and computing historical CTRs at the facet level instead of individual job level alleviate sparseness issue in historical action data. This is particularly important in job search where job lifetime is typically short. Both offline and online experiments confirm the effectiveness of the features. In offline experiment, using the entity-aware features gives improvements of +20%, +12.1%and +8.3% on Precision@1, MRR and NDCG@25, respectively. Online A/B test shows that a new model with these features is +11.3% and +5.3% better than the baseline in terms of click-through-rate and apply rate.

Filed under: Classification