关注长尾物品的推荐方法

针对推荐系统算法中覆盖率和多样性偏低所带来的长尾问题，提出了一种长尾物品的推荐框架以及关注长尾物品的推荐算法FLTI。长尾物品的推荐框架是基于卷积神经网络（CNN）模型构建的，分为数据处理层、推荐算法层和推荐列表生成层。将FLTI算法加入到了框架中的推荐算法层，该算法首先计算了频繁推荐项以及非频繁推荐项，然后采用使用长尾物品替换频繁推荐项的方法来满足系统中指定的长尾比例。实验结果表明，在Movielens 1M和BookCrossing数据集上，FLTI算法比传统的基于用户的协同过滤（UserCF）算法、基于物品的协同过滤（ItemCF）算法、奇异值分解（SVD）推荐算法以及协同去噪自动编码（CDAE）算法在覆盖率指标上最多提高了51%，多样性指标上最多提高了59%。

Recommendation method with focus on long tail items

To solve the long tail problem caused by low coverage and diversity in recommendation system， a recommendation framework for long tail items and a recommendation algorithm named FLTI （Focusing on Long Tail Item） were proposed. The recommendation framework for long tail items was built based on Convolutional Neural Network （CNN） model with three layers， including data processing layer， recommendation algorithm layer and recommendation list generation layer. The FLTI algorithm was added to the recommendation algorithm layer of the framework， and calculated the frequent recommendation items and the infrequent recommendation items at first， then replaced the frequent recommendation items by the long-tail items to meet the specified proportion of long-tail items in the system. Experimental results on Movielens 1M and BookCrossing datasets show that compared to traditional User-Based Collaborative Filtering （UserCF） algorithm， Item-based Collaborative Filtering （ItemCF） algorithm， Singular Value Decomposition （SVD） recommendation algorithm and Colloborative Denosing Auto-Encoder （CDAE） algorithm， the coverage of FLTI algorithm is improved up to 51%， and the diversity of FLTI algorithm is improved up to 59% .