CAJ | 학술논문

针对传统协同过滤推荐算法在用户评分数据极端稀疏情况下无法取得令人满意的推荐质量问题，结合User-based 和Item-based 协同过滤算法思想，提出了一种基于选择性预测策略的协同过滤推荐算法，算法利用高相似度阈值来计算用户相似性和项目相似性，并通过形成用户最近邻居集和项目最近邻居集来预测填充评分矩阵。基于Movielens数据集的实验表明，改进的算法有效改善了传统协同过滤推荐算法的数据稀疏性和扩展性问题，明显提高了系统的推荐质量。
침대전통협동과려추천산법재용호평분수거겁단희소정황하무법취득령인만의적추천질량문제，결합User-based 화Item-based 협동과려산법사상，제출료일충기우선택성예측책략적협동과려추천산법，산법이용고상사도역치래계산용호상사성화항목상사성，병통과형성용호최근린거집화항목최근린거집래예측전충평분구진。기우Movielens수거집적실험표명，개진적산법유효개선료전통협동과려추천산법적수거희소성화확전성문제，명현제고료계통적추천질량。
The user rating data in traditional collaborative filtering recommendation algorithm are extremely sparse , which re-sults in poor recommendation quality .A recommendation algorithm based on selective prediction strategy was proposed .The user-based recommendation algorithm was combined with item -based recommendation algorithm .The user similarity and the item similarity were calculated by high similarity threshold and the user -item matrix was evaluated by finding the neighbors of users and items.The experimental results based on MovieLens data set show that the improved algorithm could solve the problem of da -ta sparsity and scalability , and it could improve the accuracy of system recommendation significantly .