基于极大熵的Web服务资源个性化推荐方法

Personalized Recommendation Method of Web Service Resources Based on Maximum Entropy

当对用户行为特征推荐Web服务资源时,对于不同特征之间的相关性考虑不足,会使得推荐方法的F-Measure值较低。因此,提出一种基于极大熵的Web服务资源个性化推荐方法。根据用户历史操作记录,从用户特征、商品特征、交互特征3个角度提取用户隐式行为特征,完善用户缺失信息。根据协同过滤算法,将用户与Web资源之间的联系挖掘出来,生成用户兴趣矩阵。依托极大熵计算原理建立特征函数,明确不同用户行为特征之间的联系,并以此为基础设计Web服务资源选取算法。最后,针对用户基本属性和资源评分矩阵建立约束条件,生成个性化资源推荐方案。实验结果表明该方法的应用,使得F-Measure值与传统方法比较提升了41个百分点与33个百分点,确保推荐结果符合用户需求。

When recommending Web service resources based on user behavior characteristics,insufficient consideration of the correlation between different characteristics makes the low F-Measure value of the recommendation method.Therefore,a person⁃alized recommendation method of Web service resources based on maximum entropy is proposed.According to the historical op⁃eration records of users,the implicit behavior characteristics of users are extracted from three aspects of user characteristics,commodity characteristics and interaction characteristics to improve the missing information of users.Collaborative filtering method is used to mine the association between users and resources and generate user interest matrix.Based on the principle of maximum entropy calculation,the feature function is constructed to analyze the correlation between features,and based on this,the Web service resource selection algorithm is designed.Finally,the constraints are established according to the basic attributes of users and the resource scoring matrix,and the optimal personalized resource recommendation results are obtained.The experi⁃mental results show that compared with the recommendation method based on ontology reasoning and intelligent computing,the F-measure value is increased by 41 percentage points and 33 percentage points,and the resource recommendation results can better meet the needs of users.