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: 1611
- HTML全文浏览量: 231
- PDF下载量: 194
- 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

HTML全文

参考文献(18)

ABOWD G D, DEY A K, BROWN P J, et al. Towards a better understanding of context and context-awareness[C]. International Symposium on Handheld and Ubiquitous Computing, Karlsruhe, Germany, 1999: 304-307.

BALTRUNA L, LUDWIG B, PEER S, et al. Context relevance assessment and exploitation in mobile recommender systems[J]. Personal and Ubiquitous Computing, 2012, 16(5): 507-526. doi: 10.1007/s00779- 011-0417.

徐风苓, 孟祥武, 王立才. 基于移动用户上下文相似度的协同过滤推荐算法[J]. 电子与信息学报, 2011, 33(11): 2785-2789. doi: 10.3724/SP.J.1146.2011.00384.

XU Fengling, MENG Xiangwu, and WANG Licai. A collaborative filtering recommendation algorithm based on context similarity for mobile users[J]. Journal of Electronics Information Technology, 2011, 33(11): 2785-2789. doi: 10.3724/SP.J.1146.2011.00384.

王立才, 孟祥武, 张玉洁. 上下文感知推荐系统研究[J].软件学报, 2012, 23(1): 1-20. doi: 10.3724/SP.J.1001.2012.04100.

WANG Licai, MENG Xiangwu, and ZHANG Yujie. Context- aware recommender system: A survey of the state-of-art and possible extensions[J]. Chinese Journal of Software, 2012, 23(1): 1-20. doi: 10.3724/SP.J.1001.2012.04100.

WU W, ZHAO J, ZHANG C, et al. Improving performance of tensor-based context-aware recommenders using bias tensor factorization with context feature auto-encoding[J]. Knowledge-Based Systems, 2017, 128(C): 71-77. doi: 10.1016/j.knosys.2017.04.011.

ZOU B, LI C, TAN L, et al. GPUTENSOR: Efficient tensor factorization for context-aware recommendations[J]. Information Sciences, 2015, 299(11): 159-177. doi: 10.1016/ j.ins.2014.12.004.

WU H, YUE K, LIU X, et al. Context-aware recommendation via graph-based contextual modeling and postfiltering[J]. International Journal of Distributed Sensor Networks, 2015, 11(3): 16-26. doi: 10.1155/2015/613612.

ALHAMID M F, RAWASHDEH M, DONG H, et al. Exploring latent preferences for context-aware personalized recommendation systems[J]. IEEE Transactions on Human- Machine Systems, 2016, 46(4): 615-623. doi: 10.1109/THMS. 2015.2509965.

ALHAHYARI M and KOCHUT K. Semantic context-aware recommendation via topic models leveraging linked open data[C]. International Conference of Web Information Systems Engineering, Lugano, Switzerland, 2016: 263-277.

AIMUDENA R I, GUILLERMO J D, and MERCEDES G A. A Semantically enriched context-aware OER recommendation strategy and its application to a computer Science OER repository[J]. IEEE Transactions on Education, 2014, 57(4): 255-260. doi: 10.1109/TE.2014.2309554.

YU K, ZHANG B, ZHU H, et al. Towards personalized context-aware recommendation by mining context logs through topic models[C]. Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Kuala Lumpur, Malaysia, 2012: 431-443.

ZHU H, CHEN E, XIONG H, et al. Mining mobile user preferences for personalized context-aware recommendation [J]. ACM Transactions on Intelligent Systems Technology, 2014, 5(4): 1-27. doi: 10.1145/2532515.

YIN H, CUI B, CHEN L, et al. Modeling location-based user rating profiles for personalized recommendation[J]. ACM Transactions on Knowledge Discovery from Data, 2015, 9(3): 1-41. doi: 10.1145/2663356.

YIN H, CUI B, CHEN L, et al. Dynamic user modeling in social media systems[J]. ACM Transactions on Information Systems, 2015, 33(3): 1-44. doi: 10.1145/2699670.

BLEI D M, NG A Y, and JORDAN M I. Latent dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3(3): 993-1022.

ODIC A, TKALCIC M, KOSIR A, et al. Predicting and detecting the relevant contextual information in a movie-recommender system[J]. Interacting with Computers, 2013, 25(1): 74-90. doi: 10.1093/iwc/iws003.

施引文献

资源附件(0)

访问统计