Latency Reduction via Decision Tree Based Query Construction

LinkedIn as a professional network serves the career needs of 450 Million plus members. The task of job recommendation system is to nd the suitable job among a corpus of several million jobs and serve this in real time under tight latency constraints. Job search involves nding suitable job listings given a user, query and context. Typical scoring function for both search and recommendations involves evaluating a function that matches various elds in the job description with various elds in the member pro le. This in turn translates to evaluating a function with several thousands of features to get the right ranking. In recommendations, evaluating all the jobs in the corpus for all members is not possible given the latency constraints. On the other hand, reducing the candidate set could potentially involve loss of relevant jobs. We present a way to model the underlying complex ranking function via decision trees. The branches within the decision trees are query clauses and hence the decision trees can be mapped on to real time queries. We developed an o ine framework which evaluates the quality of the decision tree with respect to latency and recall. We tested the approach on job search and recommendations on LinkedIn and A/B tests show signi cant improvements in member engagement and latency. Our techniques helped reduce job search latency by over 67% and our recommendations latency by over 55%. Our techniques show 3.5% improvement in applications from job recommendations primarily due to reduced timeouts from upstream services. As of writing the approach powers all of job search and recommendations on LinkedIn.