融入用户长短期兴趣的推荐算法多样性优化

Diversity optimization of recommendation algorithm integrating user's long-short-term interest

多数传统的推荐算法在追求准确度时,忽略了多样性也是衡量推荐效果不可或缺的指标之一。而一味地提升多样性又势必会造成准确度的极大损失。由此提出依据用户兴趣度和兴趣变化度,在计算出用户兴趣值的基础上,分析不同用户的兴趣偏好情况。再将用户的长期与短期兴趣相结合进行推荐,保障个性化的同时确定用户的多样化程度,生成最终的推荐列表,很好地平衡了推荐结果的准确度与和多样性。

In the pursuit of accuracy,most traditional recommendation algorithms ignore diversity,which is also one of the indispensable indicators to measure the recommendation effect.However,increasing diversity desperately is bound to result in a great loss of accuracy.Presented on considering user interest and interest changes,on the basis of calculating user interest value,analyze the different user interest preference.Then combining the user's long-term and short-term interest to recommend,guaranteeing the diversification of personalized determine a user at the same time,produce the final recommendation list,reached a good balance between accuracy and diversity of recommendations.