【作者】 王龙；

【导师】 陈敏；

【作者基本信息】 华中科技大学 ， 计算机系统结构， 2015， 硕士

【摘要】 随着网络和智能设备的不断发展,互联网上的信息也越来越丰富和多样,人们可以在网络上获得各种各样的信息和服务,同时人们逐渐发现要在网上找到符合自己需求的东西也越来越麻烦,早期搜索技术可以满足人们这一需求,但是由于它的结果不够“个性化”,不能满足不同用户的个人偏好需求,因此个性化推荐技术开始被发现并应用于各个领域,推荐系统也尝试利用各种算法挖掘用户的个性化偏好。个性化推荐技术主要试图通过分析用户的历史行为对用户未购买的商品进行评分预测,并根据预测评分的高低推荐给用户相应的商品。目前基于矩阵分解的协同过滤算法虽然能够很好的进行评分预测,但是面临如何更好地挖掘和利用辅助信息的问题(如用户的个人兴趣和社交关系等),并且由于其分解后低维矩阵的不可解释性,从而无法对推荐结果进行解释。针对上述问题,通过对用户行为进行深入分析,挖掘出用户三个不同维度的个人偏好,包括利用情感分析技术从用户的评论文本中挖掘出用户的情感偏好,利用主题建模从用户的评论文本中挖掘出用户的兴趣偏好,以及根据用户的社交关系信息,计算出朋友之间的相似度,挖掘出用户的社交偏好。通过将这三种不同用户偏好与基本矩阵分解模型进行融合,然后利用随机梯度下降法进行模型求解,一方面可以提高推荐结果的准确度,另一方面由于这些不同偏好与矩阵分解模型中隐因子的融合,可以为用户提供推荐结果的解释,最后通过大量的实验,对两种基于多维度用户偏好的推荐模型进行分析,实验结果显示与传统矩阵分解模型相比,本文所提模型的准确度有较大提升。更多还原

【Abstract】 With the continuous development of network and intelligent devices, the information on the Internet is becoming more and more rich and various, people can get a variety of information and services on the Internet. At the same time, people gradually find that it is more and more difficult to find the things and services that accord with their personalized demands. Early search technology provides this service for people, but because o f its insufficient "personality", the personalized recommendation technology begins to be discovered and used in various fields, recommendation system is also trying to use a variety of algorithms for mining user’s personalized needs.Personalized recommendation technology mainly attempts to predict the user ’s rating of the commodities through the history of user ’s behavior, and recommends the corresponding goods to the user according to the predicted scores. Current collaborative filtering algorithm based on matrix decomposition shows good performance on the rating prediction, but it cannot use other auxiliary information(such as user’s personal interest and the social relations, etc.) very well, also because of its low dimensional matrix cannot be interpreted after decomposition, it is hard to explain the results of the recommendation. Therefore we deeply analyzed the user behavior, and excavated three different dimensions of user’s individual preferences, including the user’s emotional preference excavated from the user’s comment text through sentiment analysis technology, user’s interest preference excavated from the user’s text comment using LDA topic modeling, and user ’s social preferences through the user’s social relations. Then these three different user preferences are fused into the basic matrix decomposition model and the model is solved by the stochastic gradient descent method,on the one hand it can improve the accuracy of recommendation results, on the other hand, we can provide users with explanatory recommended results. Finally we designed detail experiments for the two multi-aspect user preferences based recommender model and the experimental results show that compared with the traditional matrix decomposition model, the accuracy has improved, also we can provide some explanations to the recommendation results.更多还原