Personalized Mobile App Recommendation by Learning User’s Interest from Social Media

Abstract

In social media, user interests and knowledge are vital but often overlooked resources. There are a few ways to get a sense of what people are known for, such as Twitter lists and LinkedIn Skill Tags, but most people are untagged, so their interests and expertise are effectively hidden from applications like personalised recommendation and community detection and expert mining. We obtain personalised app recommendations by learning the interest\'s association between applications and tweets by introducing an unique generative model called IMCF+ to convert user interest from rich tweet information to sparse app usage. We analyse the performance of this technique predicts the top ten apps with an 82.5 percent success rate using only 10% training data. Furthermore, in the high sparsity situation and user cold-start scenario, this purpose technique outperforms the other six state-of-the-art algorithms by 4.7 percent and 10%, demonstrating the effectiveness of our technology. All of these findings show that our method can reliably extract user interests from tweets in order to aid in the solution of the personalised app recommendation problem.

Introduction

I. INTRODUCTION

Mobile applications (apps) have made Internet services extremely accessible and convenient. Today’s Internet users prefer to use their mobile apps. App developers and network service providers will benefit from this. In order to provide higher-quality and tailored services, it is critical to understand how apps are used in various temporal and spatial settings.

While different agencies gather app usage data, there is raising concern about the privacy consequences of mining or sharing such statistics. Because of their particular interests and preferences, various users may use different collections of apps.We present a transfer learning model for the tailored app recommendation problem called Interest-aware Matrix Co-Factorization Plus in this research. This is a generative model that transfers user app usage and tweet knowledge to compensate for the lack of app usage data. We do ordinary language processing on user tweets and extract relevant terms to reflect the features that make up the user's social domain representation. We next employ matrix co-factorization to transfer the learnt interest into the app domain, allowing users' latent traits to be shared across domains. To guarantee that the statistical connection of app latent vector and word latent vector is consistent with their semantic correlation, we quantify the interest relevance of applications and words and factor it into our model. To summarise, this work makes three major contributions: We are the first to achieve personalised app assessment by transferring user tweet information from social media, which allows us to learn about user interests while compensating for the lack of app usage data. Second one, it discovers and learns the underlying latent or interest correlations between these domains, transferring the knowledge domain of textual content on social media into the domain of application usage. And last one, in the high sparsity scenario and the user cold-start problem; it surpasses the other state-of-the-art approaches by 4.7 percent and 10%, respectively, demonstrating its efficacy in tackling the challenge of individualised app recommendation.

II.  LITERATURE REVIEW

III. OPEN ISSUES

Lot of work has been done in this field because of its extensive usage and applications. In this section, some of the approaches which have been implemented to achieve the same purpose are mentioned. These works are majorly differentiated by the techniques for multi-keyword search and group sharing systems.

1. Not included in the dataset are apps that do not make any network requests, or apps that make most of their network requests via WiFi. A single network service provider is the source of all of our data.
2. Users' location privacy can be better protected by utilising spatio-temporal mismatches to enhance the usefulness of trajectory information.
3. In order to process the raw textual information contained in the check-in data, it is necessary to expand the embedding module.
4. Authors from a wide range of specialties are part of the global co-authorship network. Investigating this vast network necessitates a multidisciplinary approach.

Conclusion

We proved the capability of providing customised mobile app recommendations by transferring user tweet data from social media in this article. We tested our method\'s performance and discovered that our purpose method surpasses the other five state-of-the-art algorithms. App prediction is influenced by user and app attributes, according to the research. Our research is a step forward in transferring user data from social media to learn personal app preferences, paving the path for better-quality tailored mobile app recommendations and services for mobile consumers.

References

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Copyright

Copyright © 2022 Miss. Pratiksha Yuvraj Sawant, Mr. Mangesh D Salunke. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.