Semantic Sampling: Enhancing Recommendation Diversity and User Engagement in the Headspace Meditation App

In this paper, we present a clever approach to enhance the performance of sequential recommendation systems, specifically in the context of meditation recommendations within the Headspace app. Our method, termed “Semantic Sampling”, leverages the power of language embeddings and clustering techniques to introduce diversity and novelty in the recommen-dations. We augment the Time Interval Aware Self-Attention for Sequential Recommendation (TiSASRec) model with semantic sampling, where the next recommended item is randomly sampled from a cluster of semantically similar items. Our empirical evaluation, conducted on a sample set of 276,700 users, reveals a statistically significant increase of 2.26 % in content start rate for the treatment group (TiSASRec with semantic sampling) compared to the control group (TiSASRec alone). Furthermore, our approach demonstrates improved coverage and rarity, indi-cating a broader range of recommendations and higher novelty. The results underscore the potential of Semantic Sampling in enhancing user engagement and satisfaction in recommendation systems.