基于图嵌入和GRU的兴趣点推荐模型

POI Recommendation Model Based on Graph Embedding and GRU

下一个兴趣点推荐是基于位置的社交网络(Location-Based Social Network, LBSN)的重要服务之一,其不仅可以帮助用户寻找其感兴趣的目的地,还能帮助商家提高潜在的收入.目前已有算法提出采用用户行为序列信息以及兴趣点信息进行推荐,但其没有很好地利用兴趣点辅助信息,因此无法缓解冷启动与数据稀疏问题.本文提出了一种基于图嵌入与GRU (Gated Recurrent Unit)的兴趣点推荐模型GE-GRU (Graph Embedding-Gated Recurrent Unit). GE-GRU首先通过图嵌入的方法,将兴趣点本身与其辅助信息相融合,得到信息丰富的深层次兴趣点向量,再将其输入到神经网络中,利用GRU对用户近期兴趣偏好进行建模得到用户Embedding表示,最后根据兴趣点排序列表进行下一个兴趣点推荐.本文在一个真实的数据集Foursquare中超过48万条签到记录上进行了实验,采用Accuracy@k指标进行评估,实验结果表明, GE-GRU相比于GRU、LSTM (Long Short-Term Memory)在Accuracy@10上分别有3%和7%的提升.

The next Point-Of-Interest(POI) recommendation is one of the most important services of the Location-Based Social Network(LBSN). It can not only help users find the destination which they are interested in, but also improve the potential income of business providers. Existing algorithms have employed user behavior sequences and the POI information for recommendation, but none of them fully utilize POI side information, thereby failing to ease the problems of cold start and sparse data. In light of the above analysis, this study proposed a POI recommendation system, Graph Embedding-Gated Recurrent Unit(GE-GRU). Firstly, GE-GRU relies on Graph Embedding(GE) to integrate the POI itself with its side information to get the POI embedding that contains deep information. Then, the POI embedding is input into the GRU-based neural network to model recent user preferences to acquire user embedding. Finally, according to the POI rank list, the next POI can be recommended. Experiments are conducted on a real dataset, Foursquare, which contains more than 480 000 check-ins, and Accuracy@k is adopted for evaluation. The results show that, compared with GRU and Long Short-Term Memory(LSTM), GE-GRU has 3% and 7% improvement on Accuracy@10, respectively.