融合用户兴趣及评论效用的评论信息推荐  

Integrating User Interests with Review Helpfulness for Review Recommendation

作　　者：聂卉[1] 邱以菲 Nie Hui;Qiu Yifei(School of Information Management,Sun Yat-sen University,Guangzhou 510275)

机构地区：[1]中山大学信息管理学院,广州510275

出　　处：《图书情报工作》2021年第10期68-78,共11页Library and Information Service

基　　金：国家社会科学基金项目"面向用户感知效用的在线评论的质量与控制研究"(项目编号:15BTQ067)研究成果之一。

摘　　要：[目的/意义]Web 2.0时代,在线评论质量参差不齐和过载问题十分严重,人们从中获取有价值内容的认知成本越来越高。探究以信息推荐方式解决评论过载的有效方案,以提升网络信息利用率和信息服务质量。论文提出的评论排名推荐方案关注评论的信息质量,更强调对用户的个人信息需求的满足。[方法/过程]研究运用概率主题模型,引入词向量构建主题空间下的用户模型和评论模型,通过将其纳入评论感知效用评测系统,实现融合用户兴趣和评论质量的评论推荐,推荐效果通过系统实验予以检测。[结果/结论]实验结果表明,评论信息质量和用户个体的信息需求,共同作用于用户对评论感知效用的满意度;推荐策略实现了二者的有机融合,三组不同推荐模式下的评测效果显示,相较于单纯的"兴趣推荐"和"效用推荐","融合推荐"综合满意度得分最高。[Purpose/significance]In the Web2.0 era,the quality of online reviews is uneven and overloaded;the cognitive cost of getting valuable content from them is increasing.In the paper,we proposed a review recommendation ranking scheme which focuses on the information quality of reviews and emphasizes more on the satisfaction of users'personal information needs.[Method/process]The probabilistic topic model was adopted in this study,user-profile and review model based on the topic model were generated by employing the Word2Vector.By incorporating them into review helpfulness evaluation system,the comment recommendation which integrates user interest and comment quality was realized.The models and related methods were tested and evaluated by a serial of systematic experiments.[Result/conclusion]The results indicate that both information quality and individual information interests are of influence on review perceived helpfulness.The"combining-specific"recommendation strategy,integrating the two factors effectively,performs better than the"interest-specific"and the"utility-specific"recommendation method.From the perspective of information service,the"combining"strategy should be with the high-priority for review recommendation.

关 键 词：信息推荐 评论效用预测 用户建模 在线评论 

分 类 号：P393.1[天文地球—地球物理学] G202[文化科学—传播学]