面向个性化产品服务方案的推荐方法与应用

中国机械工程

面向个性化产品服务方案的推荐方法与应用

杨珍;耿秀丽

上海理工大学管理学院，上海，200093

出版日期:2018-08-25 发布日期:2018-08-27
基金资助:
国家自然科学基金资助项目（71301104， 51475290）；
高等学校博士学科点专项科研基金资助项目（20133120120002， 20120073110096）；
上海市教育委员会科研创新项目（14YZ088）；
上海市一流学科资助项目（S1201YLXK）
National Natural Science Foundation of China （No. 71301104， 51475290）
Specialized Research Fund for the Doctoral Program of Higher Education of China（No. 20133120120002， 20120073110096）

Recommendation Method and Applications for Personalized Product Service Plans

YANG Zhen;GENG Xiuli

Business School，University of Shanghai for Science and Technology， Shanghai，200093

Online:2018-08-25 Published:2018-08-27
Supported by:
National Natural Science Foundation of China （No. 71301104， 51475290）
Specialized Research Fund for the Doctoral Program of Higher Education of China（No. 20133120120002， 20120073110096）

摘要/Abstract

摘要： 针对传统隐语义模型（LFM）未考虑数据库动态更新从而影响方案推荐结果的问题，提出动态更新机制的加权LFM用于推荐个性化产品服务方案。针对余弦相似度计算忽略个体差异的问题，提出采用云滴距离测度与云的余弦相似度加权后的综合相似度，预测并填充空缺数据，减少数据稀疏性；采用加权LFM推荐产品服务方案，以约束新用户兴趣差异性，提高推荐精度；采用差值平均法更新推荐结果。

关键词: 个性化产品服务方案, 云滴距离测度, 余弦相似度, 加权隐语义模型

Abstract: Traditional LFM didn't consider the database dynamic update which would affect the program recommendation results，so a weighted LFM was proposed based on the dynamic update mechanism to recommend the personalized product service plans. Firstly，aiming at the problems of cosine similarity neglecting individual differences，a comprehensive similarity weighted by cosine similarity between cloud drop distance measure and cloud was proposed to predict and fill vacancy data and reduce data sparsity.Then，the difference of interests of new users could be restrained and the recommendation accuracy could be improved by using the weighted LFM recommendation service scheme. Finally，the proposed method was updated with the method of difference averaging，and the efficiency of different recommended algorithms was compared with mean absolute error （MAE）. The practicality and validity of the proposed algorithm were verified by examples.

Key words: personalized product service plan, cloud drop distance measure, cosine similarity, weighted latent factor model（LFM）

中图分类号:

TH122
N94

杨珍;耿秀丽. 面向个性化产品服务方案的推荐方法与应用[J]. 中国机械工程.

YANG Zhen;GENG Xiuli. Recommendation Method and Applications for Personalized Product Service Plans[J]. China Mechanical Engineering.

参考文献

[1]荆羽纯，葛昊，江文，等.一种基于学习自动机的推荐算法改进[J］.计算机应用研究，2016，33（1）：32-34.
JING Yuchun，GE Wei，WEN Jiang，et al.Improvement of Recommendation Algorithm Based on Learning Automata[J].Journal of Computer Applications，2016，33（1）：32-34.
[2]胡堰，彭启民，胡晓惠.一种基于隐语义概率模型的个性化Web服务方案推荐方法[J］.计算机研究与发展，2014，51（8）：1781-1793.
HU Wei，PENG Qimin，HU Xiaohui.A Recommendation Method of Personalized Web Service Scheme Based on Implicit Semantic Probability Model[J］.Journal of Computer
Research and Development，2014，51（8）：1781-1793.
[3]巫可，战荫伟，李鹰.融合用户属性的隐语义模型推荐算法[J］.计算机工程，2016，42（12）：171-175.
WU Ke，ZHAN Yinwei，LI Ying.Implicit Semantic Model Recommendation Algorithm Based on User Attributes[J］.Computer Engineering，2016，42（12）：171-175.
[4]DIXON P D R，SUNDARRAJ R P.A Latent Factor Model Based Movie Recommender Using Smartphone Browsing History[C］//International Conference on Research and Innovation in Information Systems.Langkawi， 2017：1-6.
[5]LUO X，ZHOU M，XIA Y，et al.Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models[J］.IEEE Transactions on Neural Networks & Learning Systems，2015 ，27（3）：524-537.
[6]YU Dongjin，LIU Yu，XU Yueshen，et al.Personalized QoS Prediction for Web Services Using Latent Factor Models[C］//IEEE International Conference on Services Computing.Anchorage，2014：107-114.
[7]LUO Xin，ZHOU Mengchu，LI Shuai，et al.Incorporation of Efficient Second-order Solvers into Latent Factor Models for Accurate Prediction of Missing QoS Data[J］.IEEE Transactions on Cybernetics，2017，48（4）： 1216-1228.
[8]ROSSETTI M，STELLA F，ZANKER M.Towards Explaining Latent Factors with Topic Models in Collaborative Recommender Systems[C］//24th International Workshop on Database and Expert Systems Applications.Los Alamitos ，2013：162-167.
[9]LUO R，GONG Y.Personalized Recommendation Based on Similarity of Cloud Model[C］//International Symposium on Knowledge Acquisition and Modeling.Wuhan，2009：356-359.
[10]JIA S，XU X，PANG Y，et al.Similarity Measurement Based on Cloud Models for Time Series Prediction[C］//Control and Decision Conference.Yinchuan，2016：5138-5142.
[11]ATHO F E C，TRAINA A J M，TRAINA C，et al.The Similarity Cloud Model：a Novel and Efficient Hippocampus Segmentation Technique[C］//International Symposium on Computer-based Medical Systems.Bristol， 2011：1-6.
[12]AN Xuandong，YU Xiaoqing，ZHANG Yifan.Research on the Self-similarity of Point Cloud Outline for Accurate Compression[C］//International Conference on Smart and Sustainable City and Big Data.Shanghai，2016：1-5.
[13]PAN Lina.Study on Emitter Signal Recognition Based on Backward Cloud Model and Attribute Similarity[C］//International Conference on Microwave Radar and Wireless Communications.Warsaw，2012：546-550.
[14]徐选华，王佩，蔡晨光.基于云相似度的语言偏好信息多属性大群体决策方法[J］.控制与决策，2017，32（3）：459-466.
XU Xuanhua，WANG Pei，CAI Chenguang.Multi-attribute Large Group Decision Making Method Based on Cloud Similarity for Language Preference Information[J］.Control and
Decision，2017，32（3）：459-466.
[15]张光卫，李德毅，李鹏，等.基于云模型的协同过滤推荐算法[J］.软件学报，2007，18（10）：2403-2411.
ZHANG Guangwei，LI Deyi，LI Peng，et al.Collaborative Filtering Recommendation Algorithm Based on Cloud Model[J］.Software Journal，2007，18（10）：2403-2411.
[16]YUAN Ye， LUO Xin，SHANG Mingsheng， et al.Effect of Linear Biases in Latent Factor Models on High-dimensional and Sparse Matrices from Recommender System[J］.IEEE 14th International Conference on Networking，Sensing and Control.Calabria ，2017：488-494.