基于潜在主题的混合上下文推荐算法

李平; 张路遥; 曹霞; 胡检华

doi:10.11999/JEIT170623

基于潜在主题的混合上下文推荐算法

doi: 10.11999/JEIT170623

2.
(长沙理工大学计算机与通信工程学院长沙 410114)

基金项目:

湖南省教育厅资助重点项目(14A004)

计量
- 文章访问数: 1603
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- 被引次数: 0
出版历程
- 收稿日期: 2017-06-28
- 修回日期: 2017-11-20
- 刊出日期: 2018-04-19

Hybrid Context Recommendation Algorithm Based on Latent Topic

2.
(School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China)

Funds:

The Scientific Research Fund of Hunan Provincial Education Department (14A004)

摘要

摘要: 针对单个环境上下文中项目访问记录稀疏的问题，推荐系统难以获取与当前环境上下文关联的用户偏好。该文设计了一种新的上下文关联性推荐(CTRR)算法。CTRR算法通过CTRR_LDA模型求解推荐项目出现在特定环境上下文的概率，并结合上下文后过滤推荐算法，对用户进行推荐。CTRR_LDA模型是在(LDA)模型的基础上，结合环境上下文和项目特征上下文，提出的项目与环境上下文的关联概率模型。该模型将环境上下文划分为多个环境上下文因子，每个环境上下文因子表示为K维的主题分布，挖掘环境上下文因子中项目出现的潜在主题特征。利用LDOS-CoMoDa网站上真实的电影数据集进行实验，验证了算法的可靠性。
- 推荐 /
- 关联概率 /
- 潜在主题 /
- 环境上下文 /
- 项目特征上下文
Abstract: In the recommendation system, a critical challenge is that individual environment context log may not contain sufficient item access records for mining his/her environment context preferences. This paper designs a Contextual Topic-based Relevance Recommendation (CTRR) algorithm. The CTRR algorithm uses the CTRR_LDA model and a postfiltering strategy to recommend items to users in a specific environment context. CTRR_LDA is an improved LDA model, which combines environment contexts and item feature contexts to calculate the probability of the item appeared. In this model, the environment context is divided into multiple environment context factors. Each environment context factor can be expressed as a K-dimensional topic distribution. Then the CTRR_LDA model is used to mine the latent topic of the items in each environment context factor. According to the experimental results on the LDOS-CoMoDa datasets, the reliability of algorithm is validated in the context-aware recommendation scenario.
- Recommendation /
- Relevance probability /
- Latent topic /
- Environment context /
- Item feature context

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参考文献(18)

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