Please use this identifier to cite or link to this item: https://doi.org/10.1145/2484028.2484035
Title: Addressing cold-start in app recommendation: Latent user models constructed from twitter followers
Authors: Lin, J.
Sugiyama, K. 
Kan, M.-Y. 
Chua, T.-S. 
Keywords: Cold-start problem
Collaborative filtering
Latent user models
Mobile apps
Recommender systems
Twitter
Issue Date: 2013
Source: Lin, J.,Sugiyama, K.,Kan, M.-Y.,Chua, T.-S. (2013). Addressing cold-start in app recommendation: Latent user models constructed from twitter followers. SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval : 283-292. ScholarBank@NUS Repository. https://doi.org/10.1145/2484028.2484035
Abstract: As a tremendous number of mobile applications (apps) are readily available, users have difficulty in identifying apps that are relevant to their interests. Recommender systems that depend on previous user ratings (i.e., collaborative filtering, or CF) can address this problem for apps that have sufficient ratings from past users. But for apps that are newly released, CF does not have any user ratings to base recommendations on, which leads to the cold-start problem. In this paper, we describe a method that accounts for nascent information culled from Twitter to provide relevant recommendation in such cold-start situations. We use Twitter handles to access an app's Twitter account and extract the IDs of their Twitter-followers. We create pseudo-documents that contain the IDs of Twitter users interested in an app and then apply latent Dirichlet allocation to generate latent groups. At test time, a target user seeking recommendations is mapped to these latent groups. By using the transitive relationship of latent groups to apps, we estimate the probability of the user liking the app. We show that by incorporating information from Twitter, our approach overcomes the difficulty of cold-start app recommendation and significantly outperforms other state-of-the-art recommendation techniques by up to 33%. Copyright © 2013 ACM.
Source Title: SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/78001
ISBN: 9781450320344
DOI: 10.1145/2484028.2484035
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