Automation and real-time modeling
Semantic vector search learns from the behavior of your customers to find products that match the semantic meaning of queries, rather than individual keyword matches. The modeling delivers more valuable results and addresses zero results with increased success. This automated learning process alleviates merchandisers from the burden of curating rules to address zero result searches. Signals boosting and Bayesian Personalized Ranking (BPR) are two methodologies informed by recent and real-time customer behavior which automatically determine how much each relevant product should be boosted.