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Abstract

While collaborative filtering is the dominant technique in personalized recommendation, it models user-item interactions only and cannot provide concrete reasons for a recommendation. Meanwhile, the rich side information affiliated with user-item interactions (e.g., user demographics and item attributes), which provide valuable evidence that why a recommendation is suitable for a user, has not been fully explored in providing explanations.On the technical side, embedding-based methods, such as Wide&Deep and neural factorization machines, provide state-ofthe-art recommendation performance. However, they work like a black-box, for which the reasons underlying a prediction cannot be explicitly presented. On the other hand, tree-based methods like decision trees predict by inferring decision rules from data. While being explainable, they cannot generalize to unseen feature interactions thus fail in collaborative filtering applications.In this work, we propose a novel solution named Tree-enhanced Embedding Method that combines the strengths of embeddingbased and tree-based models. We first employ a tree-based model to learn explicit decision rules (aka. cross features) from the rich side information. We next design an embedding model that can incorporate explicit cross features and generalize to unseen cross features on user ID and item ID. At the core of our embedding method is an easy-to-interpret attention network, making the recommendation process fully transparent and explainable. We conduct experiments on two datasets of tourist attraction and restaurant recommendation, demonstrating the superior performance and explainability of our solution. CCS CONCEPTS• Information systems → Recommender systems; KEYWORDS Explainable Recommendation, Tree-based Model, Embedding-based Model, Neural Attention Network * Xiangnan He is the corresponding author. This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

An interpretable outcome prediction model based on electronic health records and hierarchical attention

“…These features are designed based on in‐depth domain knowledge and scale poorly to adapt to new environment. Second, traditional machine learning technique is well known for its weakness at modeling sparse features (e.g., medical codes), since they cannot generalize to unseen feature compositions 36 . Third, they mostly use aggregated features without considering the temporality and hierarchy characteristics of the EHR data.…”

Sistema recomendador híbrido basado en modelos probabilísticos

“…Una solución llamada modelo embebido mejorado enárbol, que combina las fortalezas de los modelos embebidos y basados enárbol es presentado en (X. Wang, He, Feng, Nie, y Chua, 2018). Primero se emplea un modelo basado enárboles para aprender reglas de decisión explícita.…”