联合成对排序的物品推荐模型

Co-pairwise ranking model for item recommendation

现有的推荐模型大多仅从用户角度进行建模,忽略了物品的功能关系对用户购买决策的影响。从用户和物品这2个角度,同时考虑用户–物品之间的交互关系和物品–物品之间的功能关系,提出了联合成对排序的推荐模型。考虑正样本的排名位置和负采样策略直接影响模型收敛速度,构建一种排序感知的学习算法,用于求解所提模型的参数。实验结果表明,与当前主流推荐算法相比,该算法在多个评价指标上具有明显的性能优势。

Most of existing recommendation models constructed pairwise samples only from a user’s perspective. Nevertheless, they overlooked the functional relationships among items--A key factor that could significantly influence user purchase decision-making process. To this end, a co-pairwise ranking model was proposed, which modeled a user’s preference for a given item as the combination of user-item interactions and item-item complementarity relationships. Considering that the rank position of positive sample and the negative sampler had a direct impact on the rate of convergence, a rank-aware learning algorithm was devised for optimizing the proposed model. Extensive experiments on four real-word datasets are conducted to evaluate of the proposed model. The experimental results demonstrate that the devised algorithm significantly outperforms a series of state-of-the-art recommendation algorithms in terms of multiple evaluation metrics.