Integrating Topic and Latent Factors for Scalable Personalized Review-based Rating Prediction
Integrating Topic and Latent Factors for Scalable Personalized Review-based Rating Prediction
复制标题
整合主题和潜在因素以实现可扩展的基于评论的个性化评分预测
DOI：
10.1109/tkde.2016.2598740
复制
发表时间：
2016
影响因子：
8.9
通讯作者：
Wang Jianyong
中科院分区：
文献类型：
--
作者：
Zhang Wei;Wang Jianyong
基于评论的个性化评分预测是一个新出现的研究问题，其目的是利用现有的评论和相应的评分来推断用户对其未评分项目的评分。虽然一些研究者提出从评论文本中学习话题因素以获得评分预测的可解释性，但他们往往忽视了这样一个事实，即学习的话题因素仅限于评论文本，不能完全揭示评论和评分之间的复杂关系。此外，基于主题建模的解决方案通常使用Gibbs采样算法来学习主题和单词分布，导致不可忽略的计算负担。为了应对上述挑战，我们提出了一种综合主题和潜在因素模型(ITLFM)，该模型将主题和潜在因素以一种线性的方式结合在一起，使它们相辅相成，从而在评分预测任务中获得更好的准确性。此外，ITLFM模型通过一个加性主题模型对文本进行评论，以同时揭示用户和项目的主题因素。为了保证较高的学习效率，我们设计了一种混合随机学习算法。我们在几个标准基准上对ITLFM进行了评估，并与有代表性的方法进行了比较。实验结果表明，提出的ITLFM方法具有计算效率高、精度高、可伸缩性强等优点。
Personalized review-based rating prediction, a newly emerged research problem, aims at inferring users' ratings over their unrated items using existing reviews and corresponding ratings. While some researchers proposed to learn topic factor from review text to obtain interpretability for rating prediction, they often overlooked the fact that the learned topic factors are limited to review text and cannot fully reveal the complicated relations between reviews and ratings. Moreover, topic modeling based solutions for this problem usually utilize Gibbs sampling algorithms to learn topics and word distributions, resulting in non-negligible computational overload. To address the above challenges, we propose an integrated topic and latent factor model (ITLFM), which combines topic and latent factors in a linear way to make them complement each other for better accuracies in rating prediction tasks. In addition, ITLFM models review text through an additive topic model to reveal user's and item's topic factors simultaneously. To ensure high learning efficiency, we design a hybrid stochastic learning algorithm for ITLFM. We evaluate ITLFM on several standard benchmarks and compare with representative approaches. The experimental results demonstrate that the proposed ITLFM method is computationally efficient and accurate, as well as scalable for large scale applications.