Disentangling Item ID and Modality Effects to Improve Session-based Recommendation Accuracy and Interpretability

Core Concepts

Disentangling the distinct effects of item ID co-occurrence patterns and fine-grained item modality preferences to improve the accuracy and interpretability of session-based recommendation.

Abstract

The paper proposes a novel framework called DIMO (Disentangling ID and MOdality effects) to disentangle the effects of item ID and modality in session-based recommendation.
At the item level, DIMO introduces a co-occurrence representation schema to explicitly incorporate co-occurrence patterns into ID embeddings, and aligns different modalities (text, images) into a unified semantic space.
At the session level, DIMO presents a multi-view self-supervised disentanglement approach, including a proxy mechanism and counterfactual inference, to distinguish ID and modality effects without supervised signals.
Leveraging the disentangled causes, DIMO provides recommendations via causal inference and generates two types of explanations: co-occurrence template and feature template.
Extensive experiments on multiple real-world datasets demonstrate DIMO's consistent superiority over existing state-of-the-art methods in both accuracy and interpretability.

Stats

There are (κ) people frequently buying item x_i and recommended item x_m+1 together.
The recommended item x_m+1 also possesses feature (f2) similar to the feature (f1) of the item x_i you have bought.

Quotes

"Disentangling ID and modality effects is challenging due to the absence of supervised signals that indicate which factor dominates user choice within a session."
"Failing to distinguish rationales of user actions, existing methods fail to generate convincing explanations."

Key Insights Distilled From

Disentangling ID and Modality Effects for Session-based Recommendation

by Xiaokun Zhan... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12969.pdf

Deeper Inquiries

How can the proposed disentanglement techniques be extended to other recommendation scenarios beyond session-based settings

The proposed disentanglement techniques can be extended to other recommendation scenarios beyond session-based settings by adapting the framework to handle different types of user interactions and preferences. For example, in traditional collaborative filtering recommendation systems, where user-item interactions are the primary source of information, the disentanglement approach can be applied to separate the effects of user preferences and item characteristics. By disentangling these factors, the recommendation system can provide more personalized and accurate recommendations based on the underlying reasons for user choices. Additionally, in context-aware recommendation systems, where contextual information such as time, location, and device type influences user preferences, the disentanglement techniques can help in isolating the impact of different contextual factors on recommendations. This can lead to more effective and contextually relevant recommendations for users in diverse scenarios.

What are the potential limitations of the current disentanglement approach, and how can they be addressed in future research

One potential limitation of the current disentanglement approach is the reliance on predefined constraints and thresholds for separating ID and modality effects. These constraints may not always capture the complex and nuanced relationships between user behaviors and item features accurately. To address this limitation, future research could explore more adaptive and data-driven methods for disentanglement, such as incorporating reinforcement learning techniques to dynamically adjust the disentanglement process based on the data. Additionally, the current approach focuses on disentangling ID and modality effects, but there may be other latent factors influencing user behaviors that are not explicitly considered. Future research could investigate the incorporation of additional factors, such as user demographics or external influences, into the disentanglement framework to provide a more comprehensive understanding of user preferences and behaviors.

How can the insights from disentangling ID and modality effects be leveraged to develop more general recommendation frameworks that can handle diverse user behaviors and preferences

The insights from disentangling ID and modality effects can be leveraged to develop more general recommendation frameworks by incorporating a broader range of user behaviors and preferences. By understanding the distinct roles of item co-occurrence patterns and fine-grained preferences in user interactions, recommendation systems can be designed to adapt to various user contexts and preferences. For example, the disentanglement techniques can be used to create hybrid recommendation models that combine collaborative filtering with content-based approaches, leveraging both user-item interactions and item features for more accurate recommendations. Additionally, the insights from disentangling ID and modality effects can inform the development of explainable recommendation systems that provide transparent and interpretable recommendations to users, enhancing user trust and satisfaction with the recommendation process.