Interoperability-Enriched App Recommendation
Shi Wenxuan, Airu Yin · 2014
At present, there are three main mobile apps marketplaces, iTunes App Store, Android Market and Windows Phone Store. With app recommendation technology, users not only discover more relevant apps, but they're also more likely to be engaged with those apps on a higher level because they are relevant to their interests in the first place. Collaborative filtering (CF) methods had been applied to recommender systems, but the CF techniques do not handle sparse dataset well, especially in the case of the cold start problem where there is no enough interaction for apps. To conquer this constraint, we propose a novel recommending model: Interoperability-Enriched Recommendation (IER) that is an interoperability-enriched collaborative filtering method for multi-marketplace app recommendation based on the global app ecosystem. Experimental results on the known marketplaces app dataset demonstrate that the proposed IER method significantly outperforms the state-of-the-art CF method and context-aware recommendations (CAR) method for app recommendation, especially in the cold start scenario.