论文标题
我知道你为什么喜欢这部电影：可解释的有效的多式联运推荐
I know why you like this movie: Interpretable Efficient Multimodal Recommender
论文作者
论文摘要
最近，已经引入了有效的歧管密度估计器（EMDE）模型。该模型利用了本地敏感的哈希和计数素描算法，将它们与神经网络相结合，以在多个推荐数据集中获得最新的结果。但是，该模型摄入每个用户/会话的所有输入项目的压缩联合表示，因此通过基于梯度的方法计算单独项目的属性似乎不适用。我们证明，由于EMDE项目检索方法的属性，可以在白框设置中解释该模型。通过利用该模型的多模式灵活性，我们获得了有意义的结果，显示了多种方式的影响：文本，分类特征和图像对电影推荐输出的影响。
Recently, the Efficient Manifold Density Estimator (EMDE) model has been introduced. The model exploits Local Sensitive Hashing and Count-Min Sketch algorithms, combining them with a neural network to achieve state-of-the-art results on multiple recommender datasets. However, this model ingests a compressed joint representation of all input items for each user/session, so calculating attributions for separate items via gradient-based methods seems not applicable. We prove that interpreting this model in a white-box setting is possible thanks to the properties of EMDE item retrieval method. By exploiting multimodal flexibility of this model, we obtain meaningful results showing the influence of multiple modalities: text, categorical features, and images, on movie recommendation output.
