## 5 Expertise Retrieval
### Measuring Similarity Between Mobile Apps' Descriptions
#### 5.1.2 App Description Similarity Setup  
Transformers, Sentence Transformers and Torch Python libraries were utilized to load and deal with the pre-trained SBERT model. There are several pre-trained SBERT models, some of which are suitable for general-purpose cases, question/answer semantic searches or paraphrasing. For measuring sematic similarity between mobile apps' descriptions to discover the similarity and relatedness between specific app domains, the all-mpnet-base-v2 SBERT pre-trained model was employed. The all-mpnet-base-v2 SBERT model is a general-purpose model with the highest performance [78], therefore, it was chosen among all pre-trained SBERT models. The pre-trained model is available in [78] and Hugging Face [79]. In addition, the Genism Python library was used to load the Doc2 Vec model, and the model is publicly available on GitHub [80]. To examine the semantic similarity between app descriptions using pre-trained SBERT and Doc2 Vec models, six query app descriptions were used, which were taken from GitHub, and the descriptions are shown in Table 2. These query descriptions were taken from GitHub to avoid any bias that might arise from the researchers. Moreover, to perform a comprehensive evaluation, each description is selected from a different app domain. It is worth mentioning that we are interested in how effective these models are in our expertise retrieval component. Our main objective is using a model that can recognize the specific domain of the app descriptions (e.g., fitness app, diabetes tracking). We are not concerned about if the model can strictly consider every single feature in the descriptions, meaning that two descriptions can serve the same subdomain, however, not all features present in both descriptions can be considered as highly similar in our context. Identifying users and developers that have used similar apps is the goal, so it would not be necessary in our case to have a model that takes into account the exact similarity. The similarity is measured between the query description and all apps' descriptions in the dataset or in a certain category. Categories are important to reduce the search space, which in turn reduces processing time, by including app descriptions within the same broad domain of the query description. For instance, looking for app descriptions similar to a fitness app in the \"Finance\" domain is not reasonable, therefore, narrowing the search scope by utilizing the categories supported by the apps' stores can enhance the efficiency of the expertise retrieval process. In addition, we investigate a query case where app descriptions from all categories are used for measuring similarity. An app such as a photo-editing app could probably have similar apps that are not categorized under a single category but can be found in several categories such as \"Photo & Video\" or \"Design & Graphic\" or \"Productivity\" or \"Tools\". The six utilized queries are presented, and the descriptions are illustrated in Table 2.  
\(q_{1}:\)role = users category = Health & Fitness description = a description about a fitness app \(q_{2}:\)role = users and developers category = Games description = a description about a crossword puzzle app \(q_{3}:\)role = users and developers