计算机应用研究
計算機應用研究
계산궤응용연구
APPLICATION RESEARCH OF COMPUTERS
2013年
3期
715-719
,共5页
二阶段%相似度学习%协同过滤%既约梯度法%K-最近邻算法
二階段%相似度學習%協同過濾%既約梯度法%K-最近鄰算法
이계단%상사도학습%협동과려%기약제도법%K-최근린산법
two stages%similarity learning%collaborative filtering%reduced gradient method%K-nearest neighbor（K-NN）
针对传统的基于最近邻协同过滤推荐算法中计算相似度存在的缺陷, 提出了一种基于二阶段相似度学习的协同过滤推荐算法, 该算法旨在通过较少的迭代计算改善推荐算法性能。它以既约梯度法迭代寻优为主、最近邻算法为辅, 通过邻居的海选和精选, 最终提高了相似度的计算精度, 改善了误差性能。实验表明, 在一定条件下该算法不仅在误差性能上优于传统的推荐算法, 而且其算法收敛速度快, 可实现相似度参数动态调整和分布式计算。
針對傳統的基于最近鄰協同過濾推薦算法中計算相似度存在的缺陷, 提齣瞭一種基于二階段相似度學習的協同過濾推薦算法, 該算法旨在通過較少的迭代計算改善推薦算法性能。它以既約梯度法迭代尋優為主、最近鄰算法為輔, 通過鄰居的海選和精選, 最終提高瞭相似度的計算精度, 改善瞭誤差性能。實驗錶明, 在一定條件下該算法不僅在誤差性能上優于傳統的推薦算法, 而且其算法收斂速度快, 可實現相似度參數動態調整和分佈式計算。
침대전통적기우최근린협동과려추천산법중계산상사도존재적결함, 제출료일충기우이계단상사도학습적협동과려추천산법, 해산법지재통과교소적질대계산개선추천산법성능。타이기약제도법질대심우위주、최근린산법위보, 통과린거적해선화정선, 최종제고료상사도적계산정도, 개선료오차성능。실험표명, 재일정조건하해산법불부재오차성능상우우전통적추천산법, 이차기산법수렴속도쾌, 가실현상사도삼수동태조정화분포식계산。
In order to improve the accuracy of similarity calculation and recommendation performance in the traditional collaborative filtering recommender system, this paper proposed a collaborative filtering recommendation algorithm based on two stages of similarity learning. The algorithm took advantage of the nearest neighbor algorithm on the first stage to get candidate neighbors and used the reduced gradient method on the second stage to learn similarity. Eventually, the algorithm achieved a higher accuracy of similarity. The experimental results show that the proposed algorithm, on some conditions, not only outperforms the traditional method in terms of the error performance, but also has a fast convergence speed, which can make dynamic similarity adjustment and distributed calculation possible.
