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Semantic Search of Mobile Applications Using Word Embeddings

Authors João Coelho, António Neto, Miguel Tavares , Carlos Coutinho , Ricardo Ribeiro , Fernando Batista

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Author Details

João Coelho
  • Caixa Mágica Software, Lisbon, Portugal
  • Instituto Superior Técnico, Lisbon, Portugal
António Neto
  • Caixa Mágica Software, Lisbon, Portugal
  • University Institute of Lisbon, Portugal
Miguel Tavares
  • Caixa Mágica Software, Lisbon, Portugal
  • Lusophone University of Humanities and Technologies, Lisbon, Portugal
Carlos Coutinho
  • Caixa Mágica Software, Lisbon, Portugal
  • ISTAR-IUL, University Institute of Lisbon, Portugal
Ricardo Ribeiro
  • University Institute of Lisbon, Portugal
  • INESC-ID Lisbon, Portugal
Fernando Batista
  • University Institute of Lisbon, Portugal
  • INESC-ID Lisbon, Portugal

Funding

This work was supported by PT2020 project number 39703 (AppRecommender) and by national funds through FCT - Fundação para a Ciência e a Tecnologia with reference UIDB/50021/2020.

Cite AsGet BibTex

João Coelho, António Neto, Miguel Tavares, Carlos Coutinho, Ricardo Ribeiro, and Fernando Batista. Semantic Search of Mobile Applications Using Word Embeddings. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 12:1-12:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.SLATE.2021.12

Abstract

This paper proposes a set of approaches for the semantic search of mobile applications, based on their name and on the unstructured textual information contained in their description. The proposed approaches make use of word-level, character-level, and contextual word-embeddings that have been trained or fine-tuned using a dataset of about 500 thousand mobile apps, collected in the scope of this work. The proposed approaches have been evaluated using a public dataset that includes information about 43 thousand applications, and 56 manually annotated non-exact queries. Our results show that both character-level embeddings trained on our data, and fine-tuned RoBERTa models surpass the performance of the other existing retrieval strategies reported in the literature.

Subject Classification

ACM Subject Classification
  • Information systems → Retrieval models and ranking
  • Information systems → Document representation
  • Information systems → Language models
  • Information systems → Search engine indexing
  • Information systems → Similarity measures
  • Computing methodologies → Machine learning
Keywords
  • Semantic Search
  • Word Embeddings
  • Elasticsearch
  • Mobile Applications

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