计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
8期
123-127
,共5页
项目相似度%相似度加权%协同过滤算法%推荐系统
項目相似度%相似度加權%協同過濾算法%推薦繫統
항목상사도%상사도가권%협동과려산법%추천계통
item similarity%weighted similarity%collaborative filtering algorithm%recommended system
传统的用户相似度计算方法中每个项目的权重是相同的,然而分析传统推荐算法和现实情形,用户间共同高评分项目的权重应该高于用户间共同低评分项目的权重,并且传统用户相似度计算方法没有考虑项目间的类群关系。针对上述问题,提出了一种给项目加权的方法,从而得到考虑项目相似权重的用户相似度计算方法。通过在MovieLens数据集上进行实验,与基于传统用户相似度计算方法的协同过滤算法比较,实验结果表明,考虑了项目相似度权重的协同过滤算法能显著提高评分预测的准确性和推荐系统的质量。
傳統的用戶相似度計算方法中每箇項目的權重是相同的,然而分析傳統推薦算法和現實情形,用戶間共同高評分項目的權重應該高于用戶間共同低評分項目的權重,併且傳統用戶相似度計算方法沒有攷慮項目間的類群關繫。針對上述問題,提齣瞭一種給項目加權的方法,從而得到攷慮項目相似權重的用戶相似度計算方法。通過在MovieLens數據集上進行實驗,與基于傳統用戶相似度計算方法的協同過濾算法比較,實驗結果錶明,攷慮瞭項目相似度權重的協同過濾算法能顯著提高評分預測的準確性和推薦繫統的質量。
전통적용호상사도계산방법중매개항목적권중시상동적,연이분석전통추천산법화현실정형,용호간공동고평분항목적권중응해고우용호간공동저평분항목적권중,병차전통용호상사도계산방법몰유고필항목간적류군관계。침대상술문제,제출료일충급항목가권적방법,종이득도고필항목상사권중적용호상사도계산방법。통과재MovieLens수거집상진행실험,여기우전통용호상사도계산방법적협동과려산법비교,실험결과표명,고필료항목상사도권중적협동과려산법능현저제고평분예측적준학성화추천계통적질량。
In traditional user similarity function, the weight for each item is the same. However, analyzing traditional col-laborative filtering algorithms and practical case, the weight of user’s jointly high scoring item should be higher than the weight of user’s jointly low scoring item. And traditional user similarity function does not take taxa relationship between the items. To address the problem, it proposes a method to weight project and finally obtains a user similarity function which considers the similarity weight of items. The experimental results conducted on the movieLens data sets show that compared with the collaborative filtering algorithm which is based on the traditional user similarity function, the collabor-ative filtering algorithm which considers the similarity weight of items can significantly improve the ratings prediction accuracy and the quality of the recommendation system.