结合项目属性协作信号减少无关邻域的推荐

Recommendation for Reducing Unrelated Neighborhoods by Combining Project Attribute Collaboration Signals

在推荐系统中,知识图谱(knowledge graph,KG)作为辅助信息,提高了算法的性能以及可解释性。但在聚合多跳邻居时,它通常把所有的实体信息加以聚合并传播。KG中不是所有的信息都有助于改善推荐结果,当聚合邻域信息不加以区分时,实体的嵌入就会受到不相关实体的干扰。针对上述问题,提出一个项目属性协作信号和筛选高相关的邻域策略的模型(RUNCS),用以提高推荐的效果。具体来说,把点击过相同项目的用户称为相似邻居,通过相似邻居点击的项目和KG中的项目属性相结合,从而得到项目属性协作集;通过计算项目属性的相似性,得到相关性分数,用以筛选高相关的邻居;利用注意力机制对其分配权重进行信息聚合。在音乐和电影两个基准数据集中的实验结果表明,与现有最优主流方法相比,该模型在CTR预测上AUC提升0.6~2.7个百分点。

In the recommendation system,knowledge graph(KG) is used as auxiliary information to improve the performance and interpretability of the algorithm.However,when aggregating multi-hop neighbors,it usually aggregates and propagates all the entity information.Not all information in KG helps to improve recommendation results,and when aggregate neighborhood information is not differentiated,the embedding of entities will be interfered with by unrelated entities.Aiming at the above problems,this paper proposes a model of project attribute cooperative signals and screening highly relevant neighborhood policies(RUNCS) to improve the effect of recommendation.Specifically,firstly,the users who have clicked on the same item are called similar neighbors,and then the cooperative set of item attributes is obtained by combining the items clicked by similar neighbors with the item attributes in KG.Secondly,the similarity of item attributes is calculated to obtain the correlation score,which is used to screen the highly correlated neighbors.Finally,the attention mechanism is used to aggregate the information of its weight allocation.Experimental results on two benchmark datasets,music and film,show that compared with the existing optimal mainstream methods,the AUC of CTR forecast by this model increases by 0.6~2.7 percentage points.