计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2010年
11期
135-137,154
,共4页
日程表事件%协同过滤%个性化推荐%时间权值函数
日程錶事件%協同過濾%箇性化推薦%時間權值函數
일정표사건%협동과려%개성화추천%시간권치함수
annual schedule events%collaborative filtering%personalized recommendation%time weight function
协同过滤是个性化推荐系统中应用最广泛的推荐技术,现有的协同过滤算法不能反映出每年特定的事件与用户购买行为的关联性.针对这个问题,提出了一种考虑年度日程表事件的协同过滤算法,引入了时间权值函数,使得同一时期的越接近当前用户访问时间的近邻用户购买商品的推荐度越高,提高了协同过滤算法的推荐精度.
協同過濾是箇性化推薦繫統中應用最廣汎的推薦技術,現有的協同過濾算法不能反映齣每年特定的事件與用戶購買行為的關聯性.針對這箇問題,提齣瞭一種攷慮年度日程錶事件的協同過濾算法,引入瞭時間權值函數,使得同一時期的越接近噹前用戶訪問時間的近鄰用戶購買商品的推薦度越高,提高瞭協同過濾算法的推薦精度.
협동과려시개성화추천계통중응용최엄범적추천기술,현유적협동과려산법불능반영출매년특정적사건여용호구매행위적관련성.침대저개문제,제출료일충고필년도일정표사건적협동과려산법,인입료시간권치함수,사득동일시기적월접근당전용호방문시간적근린용호구매상품적추천도월고,제고료협동과려산법적추천정도.
Collaborative filtering is the recommendation technology which is applied most widely in the personalized recommendation system.However,existing collaborative filtering algorithms do not consider the relationship between the every year specific event and the user purchase behavior.For this reason,this paper presents a collaborative filtering algorithm considering the year schedule event.lt introduces the time weight function, makes recommendation of the commodity which purchased by the consumer of close neighbor and which's purchased time is close to current user access time and belong to the same period to be higher. So the recommendation accuracy of collaborative filtering algorithms can be improved.
