计算机时代
計算機時代
계산궤시대
Computer Era
2015年
11期
4-7
,共4页
用户行为分析%推荐算法%学习系统%数据权重%数据挖掘
用戶行為分析%推薦算法%學習繫統%數據權重%數據挖掘
용호행위분석%추천산법%학습계통%수거권중%수거알굴
user behavior analysis%recommendation algorithm%learning system%data weight%data mining
对推荐算法进行综述和分析,针对目前推荐方法的用户兴趣不明显,针对性较差等问题,提出一种基于访问时间、资源种类和心情留言的推荐算法。其中,心情留言用于衡量用户喜爱资源的程度,将该算法命名为TTM (Time-Types- Mood message)算法,并提出基于访问时间、资源种类和心情留言的三种数据权重函数。该算法在学习系统中用于对用户行为进行分析。实验证明,这种TTM算法能够做出合理的推荐,推荐质量得到了提高。
對推薦算法進行綜述和分析,針對目前推薦方法的用戶興趣不明顯,針對性較差等問題,提齣一種基于訪問時間、資源種類和心情留言的推薦算法。其中,心情留言用于衡量用戶喜愛資源的程度,將該算法命名為TTM (Time-Types- Mood message)算法,併提齣基于訪問時間、資源種類和心情留言的三種數據權重函數。該算法在學習繫統中用于對用戶行為進行分析。實驗證明,這種TTM算法能夠做齣閤理的推薦,推薦質量得到瞭提高。
대추천산법진행종술화분석,침대목전추천방법적용호흥취불명현,침대성교차등문제,제출일충기우방문시간、자원충류화심정류언적추천산법。기중,심정류언용우형량용호희애자원적정도,장해산법명명위TTM (Time-Types- Mood message)산법,병제출기우방문시간、자원충류화심정류언적삼충수거권중함수。해산법재학습계통중용우대용호행위진행분석。실험증명,저충TTM산법능구주출합리적추천,추천질량득도료제고。
For the user interest is not obvious, targeted poor and other issues in the current recommendation methods, this article reviews and analyzes the recommendation algorithms,and a recommendation algorithm based on the access time, resource type and mood message is proposed. Among them, the mood message is used to measure the extent of the user's favorite resources. The algorithm is named TTM (Time-Types-Mood message) algorithm, and three data weighting functions is proposed based on the access time, resource type and mood message. The algorithm is applied to the learning system to analyze user behavior, and make verification to itself. The experimental results show that TTM algorithm can make a reasonable recommendation; the recommendation quality has been improved.