Mobile App Recommendation

Abstract

Users can access a substantial number of apps via App Stores. Furthermore, the selection available in app stores is growing rapidly as new apps are approved and released daily. While this growth has provided users with a myriad of unique and useful apps, the sheer number of choices also makes it more difficult for users to find apps that are relevant to their interests. To solve this problem, we have proposed recommendation systems for mobile apps that employ Twitter information which can precede formal user ratings in app stores, and version information which is specific to mobile apps. In this topic, we also have developed a recommendation system for serendipitous apps using a graph-based approach between items and other approach using relationships between users.

Publication

2016

Lin, Jovian; Sugiyama, Kazunari; Kan, Min-Yen; Chua, Tat-Seng

Scrutinizing Mobile App Recommendation: Identifying Important App-related Indicators Conference

Proceedings of The 12th Asia Information Retrieval Societies Conference (AIRS 2016), 2016.

Links | BibTeX

2014

Lin, Jovian; Sugiyama, Kazunari; Kan, Min-Yen; Chua, Tat-Seng

New and Improved: Modeling Versions to Improve App Recommendation Conference

Proceedings of Special Interest Group on Information Retrieval (SIGIR '14), 2014.

Links | BibTeX

2013

Bhandari, Upasna; Sugiyama, Kazunari; Datta, Anindya; Jindal, Rajni

Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph Conference

Proceedings of the 9th Asia Information Retrieval Societies Conference (AIRS '13), 2013.

Links | BibTeX

Lin, Jovian; Sugiyama, Kazunari; Kan, Min-Yen; Chua, Tat-Seng

Addressing Cold-Start in App Recommendation: Latent User Models Constructed from Twitter Followers Conference

Proceedings of Special Interest Group on Information Retrieval (SIGIR '13), 2013.

Links | BibTeX

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