基于情境信息迁移的因子分解机推荐算法

传统推荐算法大多使用用户评分数据来推测用户偏好,仅用评分数据会导致推荐结果单一,缺乏多样性和个性化,同时评分数据还普遍存在严重的稀疏性问题.针对上述问题,提出了一种基于情境信息迁移的因子分解机推荐算法.根据情境信息对数据集进行划分,利用自适应增强方法对不同情境下的数据样本进行迁移处理,将处理后的数据集放入因子分解机,实...

Factorization Machine Recommender Algorithm Based on Context Information Transfer

Most of the traditional recommendation algorithms use user rating data to infer user preferences, which will lead to single recommendation results, lack of diversity and personalization, and there is a serious problem of sparse rating data. In response to the above questions, this paper proposes a factorization machine recommendation algorithm based on situation information transfer. The data set is divided by the situation information, and the adaptive enhancement method is used to migrate the data samples under different scenarios and the processed data set is put into the factorization machine to realize the score prediction. The experimental results show that the algorithm can fully use data samples to alleviate the sparsity problem. Meanwhile, it can make more accurate personalized recommendations. Compared with traditional recommendation algorithms, the recommendation error is reduced by 2.05%.