AppRecommender
AppRecommender: Intelligent App Distribution towards an Optimised App Discover
Description

The ubiquity of mobile devices in societies is leading to the acknowledgement of the businesses regarding the critical importance of having a mobile solution to be near their targeted audience. However, in 2017, Google Play Store had 2.8 million mobile applications available, Apple App Store had 2.2 million and the Portuguese Aptoide currently has more than 1 million apps in its repositories, which leads to a massive competition among apps. In what concerns transaction volume, in 2016 a total of 149.3 billion apps were downloaded, number that is expected to double by 2020. However, many of these downloads consist of several tries to find the right app, many apps downloaded are never used and, in 77% of the cases, apps are never used again 72 hours after the installation. This shows a considerable misalignment between the apps offered by the app stores (distribution services) and the finding of the right app from the consumers according to their needs (discovery). Due to this misalignment and the massive competition among mobile apps, Gartner predicts that less than 0.01% of mobile developers will have commercial success by the end of 2018. Moreover, in our current digital era, 52% of the apps are discovered through word-of-mouth among friends or family, and only 40% are discovered using app store’s digital services. These inefficiencies make the distribution and discovery of apps a considerable and important challenge on an industry with massive penetration in societies that is still growing fast.

To face this challenge, the AppRecommender project has the strategic goal of researching and developing technologies capable of offering the right app to the right user at the right time, optimizing thus the current app distribution and discovery services, and allowing companies to get closer to their target customers. To do so, the main technologies proposed are a fusion-based recommender system and a semantic search engine. With this strategic goal, the project aims to have specific impact on the mobile app consumers, mobile app publishers and on the Aptoide app store, lead promoter of this project that will bring the results to the market. For the consumers, the impact will be in the ease of use, efficiency and satisfaction in the discovery of apps, because of the improved alignment between their needs, characteristics and context with the apps offered by the app store. For developers and companies that promote mobile apps, the impact will be in the improved proximity to their targeted audience, optimizing the user acquisition and retention, and inherently the commercial success. For Aptoide, the impact will be on the optimized quality of service provided to consumers and companies, and in the consequent increase of the number of apps submitted to the store, active users and revenue.

Projeto financiado por

Lisboa2020

Portugal 2020

União Europeia - Fundo Europeu de desenvolvimento Regional

Internal Partners
Research Centre Research Group Role in Project Begin Date End Date
External Partners
Institution Country Role in Project Begin Date End Date
Aptoide your Android App Store (Aptoide) Portugal Leader 2019-05-01 2019-12-31
Caixa Mágica Software (Caixa Mágica) Portugal Leader 2020-01-01 2021-04-30
Project Team
Name Affiliation Role in Project Begin Date End Date
Fernando Manuel Marques Batista Professor Associado (DCTI); Local Coordinator 2019-05-01 2021-04-30
João Pedro Oliveira Professor Associado (DCTI); Integrated Researcher (IT-Iscte); Researcher 2019-05-01 2021-04-30
Ricardo Daniel Santos Faro Marques Ribeiro Professor Associado (DCTI); Researcher 2019-05-01 2021-04-30
Project Fundings
Reference/Code Funding DOI Funding Type Funding Program Funding Amount (Global) Funding Amount (Local) Begin Date End Date
39703 -- Contract União Europeia - Fundos Europeus Estruturais e de Desenvolvimento - Portugal 2020 - Portugal 318055.46 84483.54 2019-05-01 2021-04-30
Publication Outputs
Year Publication Type Full Reference
2023 Scientific journal paper Coelho, J., Mano, D., Paula, B., Coutinho, C., Oliveira, J., Ribeiro, R....Batista, F. (2023). Semantic similarity for mobile application recommendation under scarce user data. Engineering Applications of Artificial Intelligence. 121
2022 Publication in conference proceedings Paula, B., Coelho, J., Mano, D., Coutinho, C., Oliveira, J., Ribeiro, R....Batista, F. (2022). Collaborative filtering for mobile application recommendation with implicit feedback. In Morel, L., Dupont, L., and Camargo, M. (Ed.), 2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) and 31st International Association For Management of Technology (IAMOT) Joint Conference. (pp. 1065 - 1073). Nancy, France: IEEE.
2022 Publication in conference proceedings Mota, B. da., Mataloto, B. & Coutinho, C. (2022). Sustainable gardens for smart cities using low-power communications. In Morel, L., Dupont, L., and Camargo, M. (Ed.), 2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association For Management of Technology (IAMOT) Joint Conference. (pp. 1210-1216). Nancy: IEEE.
2021 Publication in conference proceedings Coelho, J., Neto, A., Tavares, M., Coutinho, C., Ribeiro, R. & Batista, F. (2021). Semantic search of mobile applications using word embeddings. In Queirós. R., Pinto, M., Simões, A., Portela, F., & Pereira, M. J. (Ed.), 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Vila do Conde/Póvoa de Varzim: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing.
2021 Publication in conference proceedings Bunga, R., Batista, F. & Ribeiro, R. (2021). From implicit preferences to ratings: Video games recommendation based on collaborative filtering. In Cucchiara, R., Fred, A., & Filipe, J. (Ed.), Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. (pp. 209-216).: SCITEPRESS – Science and Technology Publications, Lda.
2021 Publication in conference proceedings Coelho, J., Neto, A., Tavares, M., Coutinho, C., Oliveira, J., Ribeiro, R....Batista, F. (2021). Transformer-based language models for semantic search and mobile applications retrieval. In Cucchiara, R., Fred, A., & Filipe, J. (Ed.), Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. (pp. 225-232).: SCITEPRESS – Science and Technology Publications, Lda.
2020 Scientific journal paper Ribeiro, E., Teixeira, A. S., Ribeiro, R. & Matos, D. M. de. (2020). Semantic frame induction through the detection of communities of verbs and their arguments. Applied Network Science. 5 (1)
2020 Publication in conference proceedings Ribeiro, E., Teixeira, A. S., Ribeiro, R. & Matos, D. M. de. (2020). Semantic frame induction as a community detection problem. In Cherifi, H., Gaito, S., Mendes, J. F., Moro, E. and Rocha, L. M. (Ed.), Complex networks and their applications VIII. (pp. 274-285). Lisboa: Springer.
2020 Publication in conference proceedings Ribeiro, E., Ribeiro, R., Batista, F. & Oliveira, J. (2020). Using topic information to improve non-exact keyword-based search for mobile applications. In Lesot, Marie-Jeanne and Vieira, Susana and Reformat, Marek Z. and Carvalho, João Paulo and Wilbik, Anna and Bouchon-Meunier, Bernadette and Yager, Ronald R. (Ed.), Information processing and management of uncertainty in knowledge-based systems. (pp. 373-386).: Springer.
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With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific projects with the Sustainable Development Goals is now available in Ciência_Iscte. These are the Sustainable Development Goals identified for this project. For more detailed information on the Sustainable Development Goals, click here.

AppRecommender: Intelligent App Distribution towards an Optimised App Discover
2019-05-01
2021-09-30