Recommending Mobile Content Using Content Diffusion and User Preference Transition
Source: Bradley University
Recommendation accuracy is especially important in mobile e-commerce environments due to the limited screen size of mobile devices and relatively expensive connection costs. Mobile content tends to be fashionable and are geared for young users. This paper presents a novel method of building a more accurate recommender system for mobile content in a mobile ecommerce environment. The method is based on collaborative filtering, and models content diffusion and user preference transition and incorporates them in constructing pseudo ratings from implicit feedback data. In a variety of experiments, recommender systems based on the method showed significantly better recommendation accuracy than a pure collaborative filtering-based recommender system.