Improving the Personalized Recommendation in the Cold-start Scenarios

Recommender systems generate items that should be interesting for the customers. However, recommenders usually fail in the cold-start scenario - when a new item or a new customer appears. In our work, we study the cold-start problem for a new customer. For a cold-start customer we find the most similar customers and use a “their” pre-trained collaborative filtering model to recommend. We compare several recommendation approaches and similarity metrics to analyze the accuracy and computational performance.