摘要
推荐系统如今已被广泛应用于生活中,大大便利了人们的生活。传统的推荐方法主要是针对用户与物品的交互情况进行分析,分析用户与物品的历史记录,得到的只是用户过去对于物品的喜好程度。序列化推荐系统通过分析用户近一段时间与物品交互的序列,来考虑用户前后行为的关联性,能够获得用户短期内对物品的喜好程度。然而,序列化方法强调的是用户与物品在短期的联系,忽视了物品属性之间存在的关系。针对以上问题,文中提出了融合时间特性和用户偏好的卷积序列化推荐(Convolutional Embedding Recommendation with Time and User Preference, CERTU)模型。该模型能够分析物品之间存在的多样性关系,从而捕获用户对物品随时间变化的动态喜好程度这一特性。除此之外,该模型进一步考虑了物品序列中存在的单个物品和多个物品对下一物品推荐的影响。实验结果表明,CERTU模型的性能优于当前的基线方法。
At present,recommendation system has been widely used in our life,which greatly facilitates people’s life.The traditional recommendation method mainly analyzes the interaction between users and items and considers the history of users and items,and only obtains the user’s preference for items in the past.The sequential recommendation system,by analyzing the interaction sequence of items in the recent period of time and considering the relevance between the user’s previous and subsequent behaviors,can obtain user’s preference for items in short term.It emphasizes the short-term connection between user and item,while ignoring the relationship between the attributes of the item.Aiming at the above problems,this paper presents a convolutional embedding recommendation with time and user preference(CERTU)model.This model can analyze the relations between items.It can obtain dynamic changes in user preferences.The model also considers the influence of individual item and multiple items to the next item.Experiments show that the performance of CERTU model is better than that of the current baseline method.
作者
陈晋鹏
胡哈蕾
张帆
曹源
孙鹏飞
CHEN Jin-peng;HU Ha-lei;ZHANG Fan;CAO Yuan;SUN Peng-fei(School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《计算机科学》
CSCD
北大核心
2022年第1期115-120,共6页
Computer Science
基金
国家自然科学基金(61702043)。
关键词
推荐系统
卷积神经网络
序列化推荐
用户兴趣
时间特性
Recommendation system
Convolutional neural network
Sequential recommendation
User preferences
Temporal feature
