Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
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DOI：
10.1145/3471158.3472258
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Zhichao Xu;Hansi Zeng;Qingyao Ai
中科院分区：
文献类型：
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作者：
Zhichao Xu;Hansi Zeng;Qingyao Ai
现代电子商务网站包含异构信息源，例如数字评级、文本评论和图像。这些信息可用于协助推荐。通过文本评论，用户明确表达了她对该项目的喜爱程度。先前的研究人员发现，通过使用从这些评论中提取的信息，我们可以更好地描述用户的明确偏好以及项目特征，从而提高推荐性能。然而，以往的算法大多仅将评论信息用于显式反馈问题即评分预测，而当涉及到前N推荐等隐式反馈排序问题时，评论信息的使用尚未得到充分探索。看到这一差距，在这项工作中，我们研究了电子商务环境下文本评论信息对于 Top-N 推荐的有效性。我们针对 top-N 推荐任务采用了几种基于 SOTA 评论的评分预测模型，并从性能和效率方面将它们与现有的 top-N 推荐模型进行了比较。我们发现仅利用评论信息的模型无法获得比普通隐式反馈矩阵分解方法更好的性能。当利用评论信息作为正则化器或辅助信息时，可以进一步提高隐式反馈矩阵分解方法的性能。然而，利用文本评论进行电子商务 Top-N 推荐的最佳模型结构尚未确定。
Modern E-commerce websites contain heterogeneous sources of information, such as numerical ratings, textual reviews and images. These information can be utilized to assist recommendation. Through textual reviews, a user explicitly express her affinity towards the item. Previous researchers found that by using the information extracted from these reviews, we can better profile the users' explicit preferences as well as the item features, leading to the improvement of recommendation performance. However, most of the previous algorithms were only utilizing the review information for explicit-feedback problem i.e. rating prediction, and when it comes to implicit-feedback ranking problem such as top-N recommendation, the usage of review information has not been fully explored. Seeing this gap, in this work, we investigate the effectiveness of textual review information for top-N recommendation under E-commerce settings. We adapt several SOTA review-based rating prediction models for top-N recommendation tasks and compare them to existing top-N recommendation models from both performance and efficiency. We find that models utilizing only review information can not achieve better performances than vanilla implicit-feedback matrix factorization method. When utilizing review information as a regularizer or auxiliary information, the performance of implicit-feedback matrix factorization method can be further improved. However, the optimal model structure to utilize textual reviews for E-commerce top-N recommendation is yet to be determined.