Considering the wide offer of mobile applications available nowadays, effective search engines are imperative for an user to find applications that provide a specific desired functionality. Retrieval approaches that leverage topic similarity between queries and applications have shown promising results in previous studies. However, the search engines used by most app stores are based on keyword-matching and boosting. In this paper, we explore means to include topic information in such approaches, in order to improve their ability to retrieve relevant applications for non-exact queries, without impairing their computational performance. More specifically, we create topic models specialized on application descriptions and explore how the most relevant terms for each topic covered by an application can be used to complement the information provided by its description. Our experiments show that, although these topic keywords are not able to provide all the information of the topic model, they provide a sufficiently informative summary of the top- ics covered by the descriptions, leading to improved performance.

supported by Portuguese national funds through Fundação para a Ciência e a Tecnologia (FCT), with reference UIDB/50021/2020, and PT2020, project number 39703 (AppRecommender)

• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
• Languages and Literature - Humanities

This publication is an output of the following project(s):

• AppRecommender: Intelligent App Distribution towards an Optimised App Discover