CAJ | 학술논문

上下文环境和社会网络信息已经成为推荐系统所需的重要信息来源，在推荐系统中融入这些信息将进一步改进推荐系统的精度和用户满意度。为了提高用户对推荐系统的满意度，提出一种融入上下文信息与社交网络信息的个性化推荐系统CS。该算法应用随机决策树划分原始的用户－商品评分矩阵来进行上下文信息的处理，使得具有相似上下文信息的评分被分为一组。随后应用矩阵因式分解来预测用户对未评分项的预测。为了整合社交网络信息，在考虑上下文信息的环境下提出了一种融入社会网络关系的增强推荐模型，使用一种基于信任度的皮尔逊相关系数来衡量用户的相似度。在真实的实验数据集上进行验证，表明CS系统推荐较传统的基于上下文的和基于社会网络的推荐算法在性能上和推荐性能上有了很大的改善。
상하문배경화사회망락신식이경성위추천계통소수적중요신식래원，재추천계통중융입저사신식장진일보개진추천계통적정도화용호만의도。위료제고용호대추천계통적만의도，제출일충융입상하문신식여사교망락신식적개성화추천계통CS。해산법응용수궤결책수화분원시적용호－상품평분구진래진행상하문신식적처리，사득구유상사상하문신식적평분피분위일조。수후응용구진인식분해래예측용호대미평분항적예측。위료정합사교망락신식，재고필상하문신식적배경하제출료일충융입사회망락관계적증강추천모형，사용일충기우신임도적피이손상관계수래형량용호적상사도。재진실적실험수거집상진행험증，표명CS계통추천교전통적기우상하문적화기우사회망락적추천산법재성능상화추천성능상유료흔대적개선。
Contexts and social network information is valuable information for building an accurate recommender sys?tem. The merging of such information could further improve accuracy of the system and user satisfaction. This paper proposes the context and social ( CS) network, which is novel context?aware recommender system incorporating e?laborately processed social network information, in order to increase the user satisfaction on the recommendation system. The contextual information happens by applying random decision trees to partition the original user?item?rat?ing matrix such that the ratings with similar contexts are together. The matrix factorization functionality is to predict missing preference of a user for an item using the partitioned matrix. An enhanced recommendation model aided by social relationships considering the context information is proposed. A trust?based Pearson Correlation Coefficient is proposed to measure user similarity. Real datasets based experiments showed that CS enhances its performance com?pared with traditional recommendation algorithms based on context and social networks.