Mobile Phone Recommendation Based on Phone Interest

As cellular users change mobile phone frequently, mobile phone recommendation system is of great importance for mobile operator to achieve business benefit. There are essential challenges for researchers to design such system. Among them, a critical one is how to obtain and model user’s interest of mobile phone. So far, recommendation approaches based on phone’s hardware features or personalized web behavior could not achieve satisfactory results. In this paper, we propose phone interest for mobile phone recommendation. Phone interest is a latent level concept which is extracted from a group of users’ web log data, who have the same mobile phone. We propose a novel probabilistic model named “Phone Interest Model” only based on mobile web log data. All the log data are from cellular operators server, not from mobile phone’s application. The model proves its effectiveness on large scale of station cellular data from real cellular operator. In experiments, we validated the model against 1.3 billion of mobile Web logs for 4 million distinct users in Beijing metropolitan areas, and show that the model achieves a good performance in the phone recommendation, also outperforms the baseline methods and offers significantly high fidelity.