A probabilistic model to resolve diversity-accuracy challenge of recommendation systems

Understand

Recommendation systems have wide-spread applications in both academia and industry.

• Traditionally, performance of recommendation systems has been measured by their precision.
• By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic.
• Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities.