Try This Instead: Personalized and Interpretable Substitute Recommendation (2005.09344v1)

Abstract: As a fundamental yet significant process in personalized recommendation, candidate generation and suggestion effectively help users spot the most suitable items for them. Consequently, identifying substitutable items that are interchangeable opens up new opportunities to refine the quality of generated candidates. When a user is browsing a specific type of product (e.g., a laptop) to buy, the accurate recommendation of substitutes (e.g., better equipped laptops) can offer the user more suitable options to choose from, thus substantially increasing the chance of a successful purchase. However, existing methods merely treat this problem as mining pairwise item relationships without the consideration of users' personal preferences. Moreover, the substitutable relationships are implicitly identified through the learned latent representations of items, leading to uninterpretable recommendation results. In this paper, we propose attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives. Instead of directly modelling user-item interactions, we extract explicit and polarized item attributes from user reviews with sentiment analysis, whereafter the representations of attributes, users, and items are simultaneously learned. Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i.e., personalization) and item-item relationships (i.e., substitution) for recommendation. In addition, A2CF is capable of generating intuitive interpretations by analyzing which attributes a user currently cares the most and comparing the recommended substitutes with her/his currently browsed items at an attribute level. The recommendation effectiveness and interpretation quality of A2CF are demonstrated via extensive experiments on three real datasets.

• The paper introduces A2CF, a novel model that integrates user-specific attributes from reviews to deliver personalized and interpretable substitute recommendations.
• It employs collaborative filtering with deep learning to capture user-attribute and item-attribute interactions, outperforming baselines on Amazon datasets using metrics like HR and NDCG.
• The study also establishes the ATC metric to validate the interpretability of recommendations, offering actionable insights for enhancing e-commerce user satisfaction.

An Overview of A2CF: Personalized and Interpretable Substitute Recommendation

The paper "Try This Instead: Personalized and Interpretable Substitute Recommendation" presents a novel model, Attribute-Aware Collaborative Filtering (A2CF), which addresses the limitations of existing substitute recommendation systems by incorporating personalization and interpretability. This research highlights the significance of distinguishing substitutable products, which are those considered equivalent or interchangeable, and aims to enhance recommendation systems in e-commerce contexts.

Research Motivation and Core Proposal

Existing methods generally focus on pairwise item relationships, overlooking user-specific preferences and the interpretability of recommendations. The authors propose A2CF, which departs from traditional approaches by leveraging explicit item attributes, derived from user reviews, to deeply understand both user preferences and item characteristics. A2CF aims to solve two critical issues: the need for personalized recommendations that consider individual user interests and the ability to provide clear, attribute-based explanations for those recommendations.

Methodology

A key innovative aspect of A2CF is how it models interactions between users and items. Rather than relying solely on user-item interactions, A2CF decomposes these interactions into user-attribute and item-attribute interactions. Sentiment analysis extracts these attributes from user reviews, allowing the model to capture detailed preferences and apply sentiment weighting to attributes for personalized and interpretable recommendations.

A2CF employs a collaborative filtering framework with deep learning enhancements, specifically using a residual feed-forward network to learn latent representations of users, items, and attributes. The model optimizes two primary objectives:

1. Substitution Constraint: Ensuring recommended items are genuinely substitutable with the items initially considered by the user.
2. Personalization Constraint: Tailoring recommendations to match user-specific preferences by employing elaborate attribute comparisons.

Experimental Evaluation

The research includes experimental evaluations on three real-world datasets from Amazon which highlight A2CF's superior performance in recommendation effectiveness, as measured by metrics such as Hit Ratio (HR) and normalized discounted cumulative gain (NDCG), against state-of-the-art baselines like Sceptre, PMSC, SPEM, and CLVA.

Moreover, the paper verifies the interpretability of A2CF's recommendations through the novel metric of Attribute Trade-off Coverage (ATC), combining aspects of recommendation accuracy and explanation quality. The case studies presented in the paper illustrate A2CF's ability to generate meaningful and personalized explanations for its recommendations, aligning well with users' reviews and preferences.

Implications and Future Directions

The practical implications of A2CF are substantial for e-commerce platforms, where understanding and meeting user preferences can lead to increased client satisfaction and sales. The model's focus on attribute-aware recommendations potentially allows vendors to better cater to diverse consumer needs, enhancing the overall shopping experience.

From a theoretical perspective, A2CF opens new avenues for integrating explicit attributes in collaborative filtering frameworks, providing insights into enhancing recommendation predictions with interpretable outputs. The insights from user reviews represent a promising direction in augmenting collaborative systems with sentiment and feature-based data, suggesting future work may further optimize this integration for varied domains and broader datasets.

As the field progresses, potential extensions for A2CF include exploring scalability to larger datasets, real-time recommendation abilities, and cross-domain applications to verify its adaptability and robustness. These future developments could further solidify A2CF's contribution to personalized and interpretable recommendation systems in artificial intelligence research.