XPL-CF: Explainable Embeddings for Feature-based Collaborative Filtering
XPL-CF: Explainable Embeddings for Feature-based Collaborative Filtering
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DOI：
10.1145/3459637.3482221
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Faisal M. Almutairi;N. Sidiropoulos;Bo Yang
中科院分区：
文献类型：
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作者：
Faisal M. Almutairi;N. Sidiropoulos;Bo Yang
协作过滤（CF）方法在广泛的应用程序（包括推荐系统和个性化）中对我们的日常生活产生了影响。潜在因子方法，例如矩阵分解（MF）已成为CF的最新方法，但是它们缺乏可解释性，并且对其预测提供了直接的解释。解释性在推荐系统中的势头增强了，并且因为良好的解释可以摆动未定的用户。最新的可解释建议方法需要辅助数据，例如在项目评分之上进行审核文本或项目内容。在本文中，我们解决了没有其他数据可用的情况，并提出了增加CF的经典MF框架，该案例将每个用户的嵌入式编码为项目嵌入的稀疏线性组合，而对于每个项目嵌入。我们的XPL-CF方法会自动揭示这些用户项目的关系，这些关系是潜在因素的基础，并解释了如何形成结果建议。我们展示了XPL-CF对来自各个应用程序域的实际数据的有效性。我们还评估了通过数字评估和案例研究示例从XPL-CF获得的用户项目关系的解释性。
Collaborative filtering (CF) methods are making an impact on our daily lives in a wide range of applications, including recommender systems and personalization. Latent factor methods, e.g., matrix factorization (MF), have been the state-of-the-art in CF, however they lack interpretability and do not provide a straightforward explanation for their predictions. Explainability is gaining momentum in recommender systems for accountability, and because a good explanation can swing an undecided user. Most recent explainable recommendation methods require auxiliary data such as review text or item content on top of item ratings. In this paper, we address the case where no additional data are available and propose augmenting the classical MF framework for CF with a prior that encodes each user's embedding as a sparse linear combination of item embeddings, and vice versa for each item embedding. Our XPL-CF approach automatically reveals these user-item relationships, which underpin the latent factors and explain how the resulting recommendations are formed. We showcase the effectiveness of XPL-CF on real data from various application domains. We also evaluate the explainability of the user-item relationship obtained from XPL-CF through numeric evaluation and case study examples.