基于图书目录注意力机制的读者偏好分析与推荐模型研究  

Reader Preference Analysis and Book Recommendation Model with Attention Mechanism of Catalogs

作　　者：王代琳[1] 刘丽娜 刘美玲 刘亚秋[2] Wang Dailin;Liu Lina;Liu Meiling;Liu Yaqiu(Northeast Forestry University Library,Harbin 150040,China;College of Information and Computer Engineering,Northeast Forestry University,Harbin 150040,China)

机构地区：[1]东北林业大学图书馆,哈尔滨150040 [2]东北林业大学信息与计算机工程学院,哈尔滨150040

出　　处：《数据分析与知识发现》2022年第9期138-152,共15页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:61702091)的研究成果之一。

摘　　要：【目的】为解决现有推荐算法因忽略读者对于图书目录的关注而导致推荐准确度不高的问题,本文提出一种基于图书目录注意力机制的读者偏好分析方法及其个性化推荐模型IABiLSTM。【方法】根据图书标题和目录内容提取图书的语义特征:利用BiLSTM网络捕获文本的长距离依赖和语序上下文信息,使用双层Self-Attention机制增强图书目录特征更深层次的语义表达;分析读者历史浏览行为,使用兴趣函数拟合量化读者兴趣度;将图书的语义特征和读者兴趣度相结合生成读者偏好向量,计算候选图书语义特征向量和读者偏好向量的相似度预测评分并完成个性化图书推荐。【结果】使用MSE、Precision、Recall三项指标对模型进行考察,当N=50时,豆瓣数据集上结果分别为1.1%、89.1%、85.2%,Amazon数据集上结果分别为1.2%、75.2%、72.8%,优于对比模型。【局限】仅在豆瓣读书和Amazon两个数据集上进行了模型验证,在其他数据集上的泛化性能有待进一步验证。【结论】本文通过提高对图书目录的注意力关注度和对读者历史浏览交互行为的分析,有效表达读者的兴趣偏好,对图书推荐准确度的提升起到了重要作用。所提模型不仅适用于基于图书内容和读者浏览行为的推荐任务,在其他常见的自然语言处理任务中也有借鉴意义。[Objective]This paper proposes a new reader preference analysis method as well as a personalized book recommendation model(IABiLSTM),aiming to improve the accuracy of the existing algorithms.[Methods]First,we extracted the semantic features of books according to their titles and catalog contents.We used the BiLSTM network to capture the long-distance dependency of the texts and word order context information.We also utilized the Two-layer Self-Attention mechanism to enhance the deeper semantic expression of book catalog features.Then,we analyzed readers’historical browsing behaviors,which were quantified with interest function.Third,we combined the semantic features of books with readers’interests to generate their preference vector.Fourth,we calculated the similarity between the vectors of candidate books’semantic features and readers’preferences,and predicted the scores for personalized book recommendation.[Results]We examined our model on Douban Reading and Amazon datasets,and set the N value as 50.The MSE,Precision and Recall reached 1.1%,89.1%,and 85.2%,on the Douban data,while they were 1.2%,75.2%,and 72.8%with the Amazon data.These performance were better than those of the comparison model.[Limitations]More research is needed to examine our model with other datasets.[Conclusions]The proposed model improves the accuracy of book recommendation,and benefits common NLP tasks.

关 键 词：浏览行为 图书目录注意力 读者偏好 个性化推荐 BiLSTM 

分 类 号：G250[文化科学—图书馆学]