Abstract
The drastic increasing of mobile apps make users feel tough when finding Apps they need. An effective ranking service can filter many unrelated apps and provide users with a high quality result list that users need. Many existed work formed ranking service by considering semantics and built topic models and achieved good results, whereas many of them manually assigned weights when cogitating multiple features, which makes ranking result has biases. On the other hand, some of these work merely aimed at specific store of a country, therefore the algorithm cost and effectiveness when these methods are used in other countries or regions is uncertain. In this paper, we put forward an app ranking framework based on transfer learning (ARFT) which is used to rank apps for a given query and ponder relevance and quality simultaneously. ARFT as well avoided the shortage that classical ranking methods need to assign weights manually when facing multiple features and can pore over all features automatically so that ranking results become more natural. Furthermore, leveraging transfer learning, our framework fit app stores of different countries quickly and initially reflected that there exists inner attribute consistency between apps. Such consistency can largely simplify the complexity of future work in researching app stores. Experiments in real App Store dataset show that our framework can have \(50\%\) of precision and 0.96 of NDCG in top 20 Apps reflecting to the query, better than other comparison methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xu, X., Dutta, K., Datta, A., Ge, C.: Identifying functional aspects from user reviews for functionality-based mobile app recommendation. J. Am. Soc. Inf. Sci. 69(2), 242–255 (2018)
Liu, B., Kong, D., Cen, L., Gong, N.Z., Jin, H., Xiong, H.: Personalized mobile app recommendation: reconciling app functionality and user privacy preference. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (WSDM 2015), pp. 315–324. ACM (2015)
Xu, Y., et al.: Machine learning-driven apps recommendation for energy optimization in green communication and networking for connected and autonomous vehicles. IEEE Trans. Green Commun. Networking 6(3), 1543–1552 (2022)
Ma, S.P., Lee, S.J., Lee, W.T., Lin, J.H., Lin, J.H.: Mobile application search: a QoS-aware and tag-based approach. EAI Endorsed Trans. Indust. Netw. Intellig. Syst. 2(4), e6 (2015)
Rodrigues, P., Silva, I.S., Barbosa, G.A.R., Coutinho, F.R.D.S., Mourão, F.: Beyond the stars: towards a novel sentiment rating to evaluate applications in web stores of mobile apps. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 109–117. International World Wide Web Conferences Steering Committee (2017)
Maheswari, M., Geetha, S., Kumar, S.S., Karuppiah, M., Samanta, D., Park, Y.: PEVRM: probabilistic evolution based version recommendation model for mobile applications. IEEE Access 9, 20819–20827 (2021)
Tushev, M., Ebrahimi, F., Mahmoud, A.: Domain-specific analysis of mobile app reviews using keyword-assisted topic models. In: Proceedings of the 44th International Conference on Software Engineering, pp. 762–773 (2022)
Datta, A., Kajanan, S., Pervin, N.: A mobile app search engine. Mobile Networks Appl. 18(1), 42–59 (2013)
Lim, S.L., Bentley, P.J., Kanakam, N., Ishikawa, F., Honiden, S.: Investigating country differences in mobile app user behavior and challenges for software engineering. IEEE Trans. Software Eng. 41(1), 40–64 (2015)
Peltonen, E., et al.: The hidden image of mobile apps: geographic, demographic, and cultural factors in mobile usage. In: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, p. 10. ACM (2018)
Liu, B., Wu, Y., Gong, N.Z., Wu, J., Xiong, H., Ester, M.: Structural analysis of user choices for mobile app recommendation. ACM Trans. Knowl. Discov. Data (TKDD) 11(2), 17 (2016)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning (ICML 2014), pp. 1188–1196 (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Liu, Y., Liu, L., Liu, H., Li, S.: Information recommendation based on domain knowledge in app descriptions for improving the quality of requirements. IEEE Access 7, 9501–9514 (2019)
Ferrari, A., Spoletini, P., Debnath, S.: How do requirements evolve during elicitation? An empirical study combining interviews and app store analysis. Requir. Eng. 1–31 (2022)
Auch, M., Weber, M., Mandl, P., Wolff, C.: Similarity-based analyses on software applications: a systematic literature review. J. Syst. Softw. 168, 110669 (2020)
Shen, S., Lu, X., Hu, Z., Liu, X.: Towards release strategy optimization for apps in google play. In: Proceedings of the 9th Asia-Pacific Symposium on Internetware, p. 1. ACM (2017)
Noei, E., Lyons, K.: A survey of utilizing user-reviews posted on google play store. In: Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, pp. 54–63 (2019)
Datta, D., Sangaralingam, K.: Do app launch times impact their subsequent commercial success? Int. J. Big Data Intell. 3(4), 279–287 (2016)
Genc-Nayebi, N., Abran, A.: A systematic literature review: opinion mining studies from mobile app store user reviews. J. Syst. Softw. 125, 207–219 (2017)
Hassan, S., Bezemer, C.P., Hassan, A.E.: Studying bad updates of top free-to-download apps in the google play store. IEEE Trans. Software Eng. 46(7), 773–793 (2018)
Tafesse, W.: The effect of app store strategy on app rating: the moderating role of hedonic and utilitarian mobile apps. Int. J. Inf. Manage. 57, 102299 (2021)
Dąbrowski, J., Letier, E., Perini, A., Susi, A.: Analysing app reviews for software engineering: a systematic literature review. Empirical Software Eng. 27(2), 43 (2022)
Assi, M., Hassan, S., Tian, Y., Zou, Y.: Featcompare: feature comparison for competing mobile apps leveraging user reviews. Empir. Softw. Eng. 26, 1–38 (2021)
Hall, P.: The distribution of means for samples of size n drawn from a population in which the variate takes values between 0 and 1, all such values being equally probable. Biometrika 19(3/4), 240–245 (1927)
Nxx, P.: On the frequency distribution of the means of samples from a population having any law of frequency with finite moments, with special reference to pearson’s type ii. Biometrika 19(3/4), 225–239 (1927)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems (NIPS 2008), pp. 1257–1264 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Putra Santoso, S., Zhang, R. (2023). Mobile Application Ranking with Transductive Transfer Learning. In: El Abbadi, A., et al. Database Systems for Advanced Applications. DASFAA 2023 International Workshops. DASFAA 2023. Lecture Notes in Computer Science, vol 13922. Springer, Cham. https://doi.org/10.1007/978-3-031-35415-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-35415-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35414-4
Online ISBN: 978-3-031-35415-1
eBook Packages: Computer ScienceComputer Science (R0)