计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
10期
155-160,167
,共7页
苏炯铭%刘宝宏%李琦%马宏绪
囌炯銘%劉寶宏%李琦%馬宏緒
소형명%류보굉%리기%마굉서
在线评分%观点动力学%模型预测%连续观点%泊松分布%实验验证
在線評分%觀點動力學%模型預測%連續觀點%泊鬆分佈%實驗驗證
재선평분%관점동역학%모형예측%련속관점%박송분포%실험험증
online rating%opinion dynamics%model prediction%continuous opinion%Poisson distribution%experimental verification
多数观点动力学研究采用基于Agent的建模和仿真方法,与现实社会现象严重脱节。针对该问题,利用现实社会在线评分的统计数据验证和改进观点动力学模型的解释和预测能力。在评分过程中,个体的观点受到自身初始观点和群体观点的共同影响,产生的最终观点将决定个体是否加入评分群体,如果加入将产生评分行为,进而影响后续个体的观点及行为。据此过程建立一个连续观点动力学模型,对在线评分的人员数量进行预测。使用豆瓣网站的影片在线评分数据进行实验,分析各评分观点变化对在线评分数量的影响,结果表明,该模型能够有效预测在线评分人数;个体的最终观点主要受群体差-中-好评分观点的影响,而与自身初始观点基本无关;泊松参数值偏离最优值越远,预测准确率越低。
多數觀點動力學研究採用基于Agent的建模和倣真方法,與現實社會現象嚴重脫節。針對該問題,利用現實社會在線評分的統計數據驗證和改進觀點動力學模型的解釋和預測能力。在評分過程中,箇體的觀點受到自身初始觀點和群體觀點的共同影響,產生的最終觀點將決定箇體是否加入評分群體,如果加入將產生評分行為,進而影響後續箇體的觀點及行為。據此過程建立一箇連續觀點動力學模型,對在線評分的人員數量進行預測。使用豆瓣網站的影片在線評分數據進行實驗,分析各評分觀點變化對在線評分數量的影響,結果錶明,該模型能夠有效預測在線評分人數;箇體的最終觀點主要受群體差-中-好評分觀點的影響,而與自身初始觀點基本無關;泊鬆參數值偏離最優值越遠,預測準確率越低。
다수관점동역학연구채용기우Agent적건모화방진방법,여현실사회현상엄중탈절。침대해문제,이용현실사회재선평분적통계수거험증화개진관점동역학모형적해석화예측능력。재평분과정중,개체적관점수도자신초시관점화군체관점적공동영향,산생적최종관점장결정개체시부가입평분군체,여과가입장산생평분행위,진이영향후속개체적관점급행위。거차과정건립일개련속관점동역학모형,대재선평분적인원수량진행예측。사용두판망참적영편재선평분수거진행실험,분석각평분관점변화대재선평분수량적영향,결과표명,해모형능구유효예측재선평분인수;개체적최종관점주요수군체차-중-호평분관점적영향,이여자신초시관점기본무관;박송삼수치편리최우치월원,예측준학솔월저。
Most studies of opinion dynamics adopt Agent-based modeling and simulation for theoretical research and have serious gap with the real social problems. Aiming at this problem,this paper verifies and improves the interpretation and forecasting capabilities of the model with social statistical data of online rating. On the process of online rating,the individual opinion is influenced by its initial opinion and the group’ s opinions. The final opinion determines whether the individual to join the group and makes a rate or not. The rating of the individual affects the opinions and the behaviors of subsequent individuals. A simple dynamic model with continuous opinion based on this process is introduced to predict the number of personnel in online rating. It carries out experiments with the online rating data of film on the Internet website of Douban and analyses the effects of change of score proportion. Experimental results show that the model can effectively predict the number of online rating;Individual final opinion is mainly affected by the opinions of bad-normal-good in the group and almost has nothing to do with its initial opinion;The larger deviation of the Poisson parameter to optimum value leads to the lower accuracy of prediction.