Semantic similarity for mobile application recommendation under scarce user data

Abstract

The More Like This recommendation approach is ubiquitous in multiple domains and consists in recommending items similar to the one currently selected by the user, being particularly relevant when user data is scarce. We studied the impact of using semantic similarity in the context of the More Like This recommendation for mobile applications, by leveraging dense representations in order to infer the similarity between applications, based on their textual fields. Our approach was validated by comparing it to the solution currently in use by Aptoide, a mobile application store, since no benchmarks are available for this specific task. To further evaluate the proposed model, we asked 1262 users to compare the results achieved by both approaches, also allowing us to build an annotated dataset of similar applications. Results show that the semantic representations are able to capture the context of the applications, with more useful recommendations being presented to users, when compared to Aptoide’s current solution. For replication and future research, all the code and data used in this study was made publicly available, including two novel datasets (installed applications for more than one million users, and app user-labeled similarity), the fine-tuned model, and the test platform.