现代电子技术
現代電子技術
현대전자기술
MODERN ELECTRONICS TECHNIQUE
2015年
12期
8-11
,共4页
推荐系统%主动学习%Baseline SVD%样例选择
推薦繫統%主動學習%Baseline SVD%樣例選擇
추천계통%주동학습%Baseline SVD%양례선택
recommender system%active learning%Baseline SVD%instance selection
推荐系统是一种解决信息过载的新型技术，为了解决推荐系统中新用户带来的冷启动问题，提出一种基于主动学习的推荐系统。主动学习方法能有效减少需要标记的样本数量，快速建立模型，在此选择将主动学习方法和Baseline SVD推荐算法结合起来，通过记录模型训练得到的预估评价的改变程度，认为改变最大的样例即是最具有信息量的样例，供新用户标记，并重新训练模型。通过与其他选择策略进行实验比较，证实了该方法确实有效解决了新用户带来的冷启动问题。
推薦繫統是一種解決信息過載的新型技術，為瞭解決推薦繫統中新用戶帶來的冷啟動問題，提齣一種基于主動學習的推薦繫統。主動學習方法能有效減少需要標記的樣本數量，快速建立模型，在此選擇將主動學習方法和Baseline SVD推薦算法結閤起來，通過記錄模型訓練得到的預估評價的改變程度，認為改變最大的樣例即是最具有信息量的樣例，供新用戶標記，併重新訓練模型。通過與其他選擇策略進行實驗比較，證實瞭該方法確實有效解決瞭新用戶帶來的冷啟動問題。
추천계통시일충해결신식과재적신형기술，위료해결추천계통중신용호대래적랭계동문제，제출일충기우주동학습적추천계통。주동학습방법능유효감소수요표기적양본수량，쾌속건립모형，재차선택장주동학습방법화Baseline SVD추천산법결합기래，통과기록모형훈련득도적예고평개적개변정도，인위개변최대적양례즉시최구유신식량적양례，공신용호표기，병중신훈련모형。통과여기타선택책략진행실험비교，증실료해방법학실유효해결료신용호대래적랭계동문제。
Recommender system is a new technology to deal with information overload. In order to solve the cold start prob?lem in recommender system,which is brought about by new users,a recommender system based on active learning is presented in this paper. The active learning method can create the model quickly because it can effectively reduce the quantity of training samples needing to be marked. The combination of the active learning method and Baseline SVD recommendation algorithm is adopted in this paper. The change of estimation evaluation is obtained by recording model’s training. The sample which changes most is regarded as the one which is the most informative. Compared with other instance selection strategies,experimental re?sults show that the method can accelerate the speed of cold start brought about by new users.
