基于图表示学习的会话感知推荐模型

根据历史记录预测用户的下一次点击(即基于会话的推荐)是推荐系统中一个重要的子任务.重点研究会话推荐中如何在不牺牲预测准确性的情况下缓解用户的兴趣漂移问题，提高用户满意度.基本思想是从全局统计的角度出发，建立一个用于表示物品先后点击顺序的物品依赖关系图，据此提出一种图表示学习算法，生成可以保留关联物品间复杂关联关系的物品向量表达，最后，基于长/短期记忆机制，将物品向量表达作为“固定”输入，从而构建一个可以同时捕捉用户长期兴趣和短期兴趣的会话感知推荐模型.不同于其他相关工作，首次提出将下一次点击预测模型建立在“固定”物品表达的基础上.在公开数据集上的实验结果表明：提出的推荐模型在预测准确性和推荐多样新颖性上的表现优于其他相关方法.

Graph Embedding Based Session Perception Model for Next-Click Recommendation

Predicting users’ next-click according to their historical session records, also known as session-based recommendation, is an important and challenging task and has led to a considerable amount of work towards this aim. Several significant progresses have been made in this area, but some fundamental problems still remain open, such as the trade-off between users’ satisfaction and predictive accuracy of the models. In this study, we consider the problem of how to alleviate user interests drift without sacrificing the predictive accuracy. For this purpose, we first set up an item dependency graph to represent the click sequence of items from a global, statistical perspective. Then an efficient graph embedding learning algorithm is proposed to produce item embeddings which preserve the information flow properties of the system and the structural dependency between each pair of items. Finally, the proposed model is capable of capturing the users’ general interests and their temporal browsing interests simultaneously by using of a BiLSTM based long/short term memory mechanism. Experimental results on two real-world data sets show that the proposed model not only performs better in terms of predictive accuracy but also demonstrates better diversity and novelty in its recommendations as compared with other state-of-the-art methods.