注意力感知的群组Next事件推荐策略

Attention-aware Next Event Recommendation Strategy for Groups

近年来,基于事件社会网络(EBSN)逐渐成为人们寻找感兴趣事件的有效途径,如何将事件精准地推荐给有需求的用户已成为该领域的重要主题。下一个项目推荐能够捕获用户的动态偏好,在电子商务等领域取得较好推荐效果。然而,鲜见有关EBSN中的面向群组的下一个(Next)事件推荐研究。主要研究面向群组的Next事件推荐策略,但由于群组偏好会发生动态变化,且事件生命周期短、新事件冷启动等问题使得针对群组进行Next事件推荐变得更加困难。首先,针对群组偏好会随时间发生动态变化的特征,将群组与事件的历史交互划分为多个时段。考虑到划分后群组成员数据变得更加稀疏,不利于群组偏好建模,采用基于参与度的排序策略提取当前时段核心成员的成员偏好,并利用注意力机制融合出群组静态偏好。然后,通过序列模型将各个时段的静态偏好融合得到群组动态偏好。最后,将事件推荐视为多标签分类问题,即将上下文看作事件的多个标签,通过预测各个上下文的概率分布以匹配事件,从而有效缓解新事件冷启动问题。实验结果表明,所提出的推荐策略具有较好的性能。

In recent years,event-based social networks(EBSN)have gradually become an effective way for people to choose social events,and how to accurately recommend events to users or groups in need has become an important topic in this field.Next item recommendation can capture users’dynamic preferences and has been well developed in e-commerce and other fields.However,there are less researches on next event recommendation for groups in EBSN.This paper mainly studies the group-oriented next event recommendation strategy,but due to the dynamic change of group preference,short event life cycle and cold start of new events,it is more difficult to recommend the next event for groups.Firstly,based on the characteristic that group preferences change dynamically over time,the history interaction records of group are divided into each period.Considering the sparsity of the member data due to the period division,which is unfavorable to group preference modeling,an engagement-based ranking strategy is proposed to extract the preferences of core members in the current period,and the attention mechanism is used to fuse them to the group static preferences.Then,the group dynamic preference is obtained by combining the static preference of each period with the attention-based sequential model.Finally,the multi-label classification problem is introduced into the event recommendation,which regards the contexts as labels of the event,and makes the model predict the probability distribution of each context to match the event,so as to alleviate the new event cold start problem.Experimental results verify that the proposed strategy has good performance.