基于新奇度量的社交事件推荐方法

Social event recommendation method based on unexpectedness metric

在社交事件网络(EBSN)中,推荐工作都是从用户的历史喜好出发建模用户偏好,阻碍了用户接触新事物的范围和途径。针对上述问题,提出基于新奇度量的社交事件推荐模型UER(Unexpectedness-based Event Recommendation)。UER模型包括Base和Unexpected两个子模型,首先,Base子模型基于用户、事件以及用户历史事件交互序列特征,通过注意力机制衡量事件在用户历史喜好中的权重,最终预测用户参加事件的概率;其次,Unexpected子模型通过自注意力机制提取用户的多个兴趣表示来计算用户自身新奇度和候选事件对用户的新奇值,从而衡量推荐事件的新奇程度。在Meetup-加州数据集上,UER模型相较于DIN(Deep Interest Network)和PURS(Personalized Unexpected Recommender System)的推荐命中率(HR)分别提高22.9%和30.3%,归一化折损累积收益(NDCG)分别提高27.5%和42.3%,推荐事件的新奇程度分别提高54.5%和21.4%;在Meetup-纽约数据集上,UER模型相较于DIN和PURS的HR分别提高18.2%和21.8%,NDCG分别提高26.9%和32.0%,推荐事件的新奇程度分别提高52.6%和20.8%。

In Event-Based Social Network(EBSN),the recommendation work starts from the user historical preferences to model user preferences,which hinders the scope and ways for users to access new things.Aiming at the above problems,an unexpectedness metric-based social event recommendation model was proposed,namely UER(Unexpectedness-based Event Recommendation).UER model included two sub-models,Base and Unexpected.Firstly,based on the interaction sequence characteristics of users,events,and user historical events,the Base sub-model used the attention mechanism to measure the weights of events in user historical preferences,and finally predicted the probabilities of users participating in events.Secondly,multiple interest representations of the user were extracted by Unexpected sub-model through the selfattention mechanism to calculate the unexpectedness of the user itself and the unexpectedness value of the candidate event to the user according to the multiple interest representations of the user,so as to measure the unexpectedness of the recommended event.Experimental results on Meetup-California dataset show that compared with Deep Interest Network(DIN)and Personalized Unexpected Recommender System(PURS),the recommendation Hit Ratio(HR)of the UER model is increased by 22.9% and 30.3%,the Normalized Discounted Cumulative Gain(NDCG)is increased by 27.5% and 42.3%,and the unexpectedness of recommended events is increased by 54.5% and 21.4% respectively.On Meetup-NewYork dataset,the recommendation HR of the UER model is increased by 18.2% and 21.8%,the NDCG is increased by 26.9% and 32.0%,and the unexpectedness of recommended events is increased by 52.6% and 20.8% respectively.