Computer Science ›› 2019, Vol. 46 ›› Issue (6): 75-79.doi: 10.11896/j.issn.1002-137X.2019.06.010

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Recommendation Strategy Based on Trust Model via Emotional Analysis of Online Comment

LU Zhu-bing1, LI Yu-zhou2   

  1. (College of Economics and Management,Southwest University,Chongqing 400715,China)1
    (College of Computer and Information Science,Southwest University,Chongqing 400715,China)2
  • Received:2018-11-10 Published:2019-06-24

Abstract: Personalized recommendation technology has become a very effective approach to cope with “information overload” in E-commerce.Aiming at the problems of data sparseness and cold-start in traditional collaborative filtering recommender system,which have led to the decline of the accuracy in recommendation,weakened user’s confidence towards the system,this paper proposed a new recommendation strategy using trust theory in sociology to offer users better personalized service. From this perspective,user’s online comments to the items that they have experienced are analyzed,the user’s emotional tendency is extracted,and it is effectively quantified.The trust relationship between users is grown by analyzing the similarity of user’s emotional tendency.At the same time,users’ rating data are combined to compensate for the lack of recommendation factor caused by similarity as the only preference weight.The work in this paper includes three parts:analysis and quantification of user emotional tendency based on online reviews,mo-deling of trust relationship based on similarity between emotion and design of recommendation strategy based on trust relationship.Experiments show that the proposed recommendation strategy can effectively reduce the average absolute error value called MAE,which means the recommendation accuracy is improved.At the same time,the coverage rate is also effective increased,which means that the system has more items to recommend.Additionally,the management mechanism of trust relationship can also greatly enhance user’s personalized experience of the system and user’s confidence to the system.

Key words: Collaborative filtering, Data sparsity, Trust relationship, Emotion analysis, Similarity

CLC Number: 

  • TP391
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