中文摘要：

随着互联网技术的普及和现代电子商务的迅速发展,推荐系统得到广泛使用,但大多数推荐算法仍存在冷启动、可解释性差两大问题.本文以结合评分和评论信息的HFT模型为基础,提出了一个改进的HFT模型,通过加入自由向量,捕获原HFT模型中未出现的评论信息,缓解了这两大问题,并进一步提高了模型的准确度.最后,通过两个大型数据集的实验,结果表明本文的模型准确度优于HFT模型,可为有效利用评论信息资源提供参考.

英文摘要：

With the Internet technology and modern E-commerce becoming popular,the recommender system has been widely used,but two problems of most recommendation algorithms still remain,i.e.cold start and explanatory problem.Based on the HFT(Hidden Factors and Hidden Topics)model which combines the review and rating information,an improved HFT model is proposed.By adding the free vector in order to capture the review information not discussed in the HFT model,the two problems can be released and the model accuracy improved.At last,the two large datasets shows that the proposed model is better than the HFT model in accuracy,which can largely benefit the use of review information.