CAJ | 학술논문

针对电子商务推荐系统中，互联网“信息过载”所造成的难以准确定位用户兴趣并提供准确品牌推荐的问题，通过深入挖掘电子商务网中的用户行为日志，抽取出能辨别出用户对商品品牌购买行为的多个特征，然后将这些特征融入到梯度渐进回归树算法中，建立用户兴趣偏好模型来提高推荐精度。实验结果表明，在数据稀疏的情况下，该算法仍能较好的识别出用户对品牌的偏好，并在推荐准确度方面较其他传统推荐和分类算法有明显的提高。
침대전자상무추천계통중，호련망“신식과재”소조성적난이준학정위용호흥취병제공준학품패추천적문제，통과심입알굴전자상무망중적용호행위일지，추취출능변별출용호대상품품패구매행위적다개특정，연후장저사특정융입도제도점진회귀수산법중，건립용호흥취편호모형래제고추천정도。실험결과표명，재수거희소적정황하，해산법잉능교호적식별출용호대품패적편호，병재추천준학도방면교기타전통추천화분류산법유명현적제고。
In E-commerce recommendation system, “Information overload” on Internet has brought a tough problem, which is how to precisely position users’ interest and provide users with accurate brand recommendation. To solve this problem, in this paper, many features which could describe the purchasing behavior of users are extracted by deeply mining large-scale of user behavior logs. A brand preference model was constructed by applying these features into Gradient Boosting Regression Tree algorithm, to improve accuracy of the recommendation algorithm. Experiment results show that, in condition of sparse data, algorithm in this paper can still fit brand preference of users very well, and has significantly improvement in accuracy compared with traditional recommendation and classification algorithm.