Graph-based Extractive Explainer for Recommendations
Graph-based Extractive Explainer for Recommendations
复制标题
DOI：
10.1145/3485447.3512168
复制
发表时间：
2022-02
期刊：
影响因子：
--
通讯作者：
Peifeng Wang;Renqin Cai;Hongning Wang
中科院分区：
文献类型：
--
作者：
Peifeng Wang;Renqin Cai;Hongning Wang
推荐系统中的查询帮助用户在一组推荐项目中做出明智的决定。广泛的研究关注已经致力于生成自然语言解释来描述推荐是如何生成的以及为什么用户应该关注它们。然而，由于这些解决方案的不同限制，例如，基于模板或基于生成，很难同时使解释容易感知，可靠和个性化。在这项工作中，我们开发了一个图形注意神经网络模型，无缝集成用户，项目，属性和句子提取为基础的解释。选择项目的属性作为中介，以促进消息传递，用于句子相关性的用户项目特定评估。为了平衡单个句子的相关性、整体属性覆盖率和内容冗余度，我们解决了一个整数线性规划问题来进行句子的最终选择。针对两个基准审查数据集的一组最先进的基线方法进行了广泛的实证评估，证明了所提出的解决方案的生成质量。
Explanations in a recommender system assist users make informed decisions among a set of recommended items. Extensive research attention has been devoted to generate natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable, and personalized at the same time. In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes and sentences for extraction-based explanation. The attributes of items are selected as the intermediary to facilitate message passing for user-item specific evaluation of sentence relevance. And to balance individual sentence relevance, overall attribute coverage and content redundancy, we solve an integer linear programming problem to make the final selection of sentences. Extensive empirical evaluations against a set of state-of-the-art baseline methods on two benchmark review datasets demonstrated the generation quality of proposed solution.