CAJ | 학술논문

随着因特网的飞速发展，电子商务网站为人们提供了越来越多的选择，随之而来的信息过载和信息迷失问题日益严重，个性化推荐系统的出现极大地改善了这一情况。协同过滤是目前主流的推荐算法，但随着用户物品数目的日益增多和系统规模的不断扩大，用户-物品评分矩阵存在着严重的稀疏性等问题，导致推荐系统的推荐质量严重下降。针对此问题，文中提出了一种改进的协同过滤推荐算法，将评论挖掘技术引入协同过滤算法中，量化物品在各个特征上的分数，然后结合物品特征和用户评分共同计算物品相似度，将得到的物品预测评分填充用户-物品评分矩阵，最后结合基于用户的协同过滤思想对用户产生推荐。实验结果表明，改进的协同过滤推荐算法提高了推荐结果的精确度。
수착인특망적비속발전，전자상무망참위인문제공료월래월다적선택，수지이래적신식과재화신식미실문제일익엄중，개성화추천계통적출현겁대지개선료저일정황。협동과려시목전주류적추천산법，단수착용호물품수목적일익증다화계통규모적불단확대，용호-물품평분구진존재착엄중적희소성등문제，도치추천계통적추천질량엄중하강。침대차문제，문중제출료일충개진적협동과려추천산법，장평론알굴기술인입협동과려산법중，양화물품재각개특정상적분수，연후결합물품특정화용호평분공동계산물품상사도，장득도적물품예측평분전충용호-물품평분구진，최후결합기우용호적협동과려사상대용호산생추천。실험결과표명，개진적협동과려추천산법제고료추천결과적정학도。
With the rapid development of the Internet,electronic commerce website provide more choice for people,but information over-load and information lost problems become increasingly serious,the personalized recommendation system has greatly improved this situa-tion. Collaborative filtering recommendation algorithm is a popular recommendation algorithm,but with the increasing number of items and users and the continuous expansion of the system,the serious user-item rating matrix sparse problem leads to a lower recommended quality. In order to solve this problem,put forward an improved collaborative filtering recommendation algorithm,which introduces com-ments mining technology into collaborative filtering algorithm to get the item score on each feature,and then combine the feature of items and the user score to calculate the item similarity,fill the predicted rating score into the user-item rating matrix,finally recommend to the user based on the user-based collaborative filtering ideas. The experimental result shows that the improved collaborative filtering recom-mendation algorithm improves the precision of the recommendation results.