基于消极相似性的自适应社会化推荐

Adaptive social recommendation based on negative similarity

社会化推荐旨在融合社会关系改善传统推荐算法的推荐效果。当前基于网络嵌入(NE)的社会化推荐算法面临两个问题:一是在构建网络时未考虑对象间的不一致性,并且倾向于利用获取难度大、约束条件多的积极对象来约束算法;二是这些算法未能依据评分数量消除算法训练中的过拟合。因此,提出一种基于消极相似性的自适应社会化推荐(ASRNS)算法。首先通过一致性分析构建具有正向相关性的同构网络;接着联合加权随机游走与Skip-Gram算法得到嵌入向量;然后计算相似度,并从消极相似性的角度来约束矩阵分解(MF)算法;最后基于自适应机制将评分数量映射到理想评分数量区间,并对算法偏置项施加不同的惩罚。在FilmTrust和CiaoDVD数据集上实验结果表明,与协同用户网络嵌入(CUNE)算法、一致性邻居聚合的推荐(ConsisRec)算法等算法相比,ASRNS的均方根误差(RMSE)分别至少降低了2.60%和5.53%,平均绝对误差(MAE)分别至少降低了1.47%和2.46%。可见,ASRNS不仅可以有效降低评分预测误差,还能显著改善算法训练过程中的过拟合问题,对不同评分数量的对象都具有较好的健壮性。

Social recommendation aims to improve recommendation effect of traditional recommendation algorithms by integrating social relations.Currently,social recommendation algorithms based on Network Embedding(NE)face two problems:one is that inconsistency between objects is not considered when constructing network,and algorithms are often restricted by positive objects that are difficult to obtain and have many constraints;the other is that the elimination of overfitting in algorithm training process based on the number of ratings cannot be realized by these algorithms.Therefore,an Adaptive Social Recommendation algorithm based on Negative Similarity(ASRNS)was proposed.Firstly,homogeneous networks with positive correlations were constructed by consistency analysis.Then,embedded vectors were obtained by combining weighted random walk with Skip-Gram algorithm.Next,similarities were calculated,and Matrix Factorization(MF)algorithm was constrained from the perspective of negative similarity.Finally,the number of ratings was mapped to the ideal rating range based on adaptive mechanism,and different penalties were imposed on bias terms of the algorithm.Experiments were conducted on FilmTrust and CiaoDVD datasets.The results show that compared with algorithms such as Collaborative User Network Embedding(CUNE)algorithm and Consistent neighbor aggregation for Recommendation(ConsisRec)algorithm,ASRNS has the Root Mean Square Error(RMSE)reduced by at least 2.60%and 5.53%respectively,and the Mean Absolute Error(MAE)reduced by at least 1.47%and 2.46%respectively.It can be seen that ASRNS can not only reduce rating prediction error effectively,but also improve over-fitting problem in algorithm training process significantly,and has good robustness for objects with different ratings.