CAJ | 학술논문

针对当前协同过滤推荐算法易受数据稀疏性与冷启动的问题，提出了一种改进最近邻的协同过滤推荐算法。建立用户-项目评分矩阵，并度量项目之间、用户之间的相似性，获取项目和用户的最近邻居，其中最近邻居的最优参数k值采用粒子群算法选择，在MovieLens和Book-Crossing数据集上进行了仿真对比实验。结果表明，相对于其他协同过滤推荐算法，该算法降低了平均绝对误差值，提升了推荐准确度，达到提高推荐质量效果的目的。
침대당전협동과려추천산법역수수거희소성여랭계동적문제，제출료일충개진최근린적협동과려추천산법。건립용호-항목평분구진，병도량항목지간、용호지간적상사성，획취항목화용호적최근린거，기중최근린거적최우삼수k치채용입자군산법선택，재MovieLens화Book-Crossing수거집상진행료방진대비실험。결과표명，상대우기타협동과려추천산법，해산법강저료평균절대오차치，제승료추천준학도，체도제고추천질량효과적목적。
Aiming to the problems that the quality and precision are caused by the sparse user scorings and cold-start, a novel collaborative filtering algorithm based on improved nearest neighbors is proposed in this paper. User-item matrix is established, and similarity between items and users is measured, the nearest neighbor of items and users is acquired, in which the particle swarm optimization algorithm is used to select the optimal value of the parameter k, the simulation experi-ments are carried out on MovieLens and Book-Crossing dataset. The results show that the proposed algorithm can achieve lower MAE and efficiently improve recommendation precision, and it can enhance the quality of recommendations.