结合社交影响和长短期偏好的个性化推荐算法

Personalized recommendation algorithm combining social influence and long short-term preference

针对基于会话的推荐算法只捕获用户的短期动态兴趣,忽略长期兴趣和社交好友对用户行为的影响,提出结合社交影响和长短期偏好的推荐算法.设计新颖的异构关系图来组织用户的社交关系和历史会话,提出基于注意力机制的异构图神经网络对图进行学习,得到融合用户社交影响的长期偏好.针对社交影响力不一致容易引入噪声的问题,提出加权剪枝策略,减少了噪声干扰且丰富了图结构信息.利用无损的会话建模方法捕获用户的短期偏好,将短期偏好与长期偏好进行自适应融合,得到反映用户全局偏好的特征表示.Gowalla和Delicious数据集上的实验结果表明,所提方法的各项指标相比现有先进方法均有显著提升,证明了所提算法的有效性.

Session-based recommendation algorithms only capture users’short-term dynamic interests,ignoring the impact of long-term interests and social friends on their behavior.To address the problem,a recommendation algorithm combining social influence and long short-term preferences was proposed.Firstly,a novel heterogeneous relation graph was designed to organize users’social relations and historical interaction behaviors.And a heterogeneous graph neural network based on the attention mechanism was proposed to learn the graph,and to obtain long-term preference for integrating social influence of users.Moreover,considering the problem of noise caused by inconsistent social influence,a weighted and pruning strategy was proposed to reduce noise interference and enrich the graph structure information.Then,a lossless session modeling method was used to capture users’short-term preference.Finally,users’short-term preference and long-term preference were adaptively fused to obtain a feature representation that reflects users’global preferences.Experimental results on Gowalla and Delicious datasets show that the indicators of the proposed method are significantly improved compared with the existing advanced methods,which proves the effectiveness of the proposed algorithm.