推荐系统中的新颖性问题研究

准确性推荐中存在商品类型单一、流行商品多、缺乏新意的问题，因而新颖性推荐得到重视。但已有研究在设计算法时未考虑项的特征，无法针对不同用户帮其区分和挑选具备较高新颖度的项。为提高推荐系统的性能，对基于随机游走的方法进行改进，提出融合新颖性特征的推荐算法。从兴趣扩展和预测角度分析项的特征，给出完善的新颖度定义，并结合用户需求构建新的转移概率，产生个性化的推荐列表，提高了列表内容的新意。实验结果表明，提出的算法较现有算法对准确率影响较小，同时在新颖性指标上有明显提升，并得出通过融合新颖性特征能够在兼顾准确性的情况下有效改善推荐内容的结论。

Research on novelty problems in recommendation systems

Focusing on problems of the accuracy recommendation system that the recommended commodity type is relatively single, and commodities are mostly popular goods and lack of freshness, the novelty recommendation is gradually gaining attention. However, current researches don''t combine item features when designing algorithms, which make them unable to distinguish and select items with higher novelty for different users. In order to improve the performance of the recommendation system, this paper improved the method based on random walk and designed a new recommendation algorithm by fusing novelty features. This algorithm further analyzed features of items and gave the formal definition of the novelty from perspectives of user interest expansion and prediction. This paper analyzed user demands, constructed new transition probability, generated personalized recommendation lists and improved the novelty of the lists. The experimental results show that the proposed algorithm has less influence on the accuracy than existing methods and has significant improvement on novelty indexes. It concludes that by fusing novel features, this system can improve the recommendation contents effectively while taking into account the accuracy.