Addressing Missing Modalities in Multimodal Recommendation: A Feature Propagation-based Approach

Core Concepts

The core message of this paper is to propose a feature propagation-based approach, called FeatProp, to address the problem of missing modalities in multimodal recommendation.

Abstract

The paper introduces and formalizes the problem of missing modalities in multimodal recommendation, where a portion of items in the catalogue lack multimodal content (e.g., product images, textual descriptions) to extract meaningful features from. To address this issue, the authors propose to reshape the missing modalities problem as a problem of missing graph node features, and leverage the state-of-the-art FeatProp algorithm to impute the missing multimodal features.
Specifically, the authors first project the user-item bipartite graph into an item-item co-interaction graph, based on the assumption that co-interacted items share similar multimodal content. Then, they apply a modified version of the FeatProp algorithm to propagate the available multimodal features and impute the missing ones. The reconstructed multimodal features are finally used as input to power any multimodal recommender system.
The authors conduct an extensive experimental evaluation, comprising 1080 settings across 3 recommendation datasets, 2 multimodal recommender systems, 4 methods to impute missing modalities, and 9 percentages of missing items. The results show that FeatProp consistently outperforms other shallower baselines, especially when the percentage of missing items is not too high. Additionally, the authors investigate the impact of the number of propagation layers, finding that not all multimodal recommender systems may require the same number of layers to converge.

Stats

The datasets contain between 19,412 and 35,598 users, 7,050 and 18,357 items, and 139,110 and 256,308 interactions, with sparsity ranging from 99.899% to 99.961%.
The items are described by 4096-dimensional visual features and 1024-dimensional textual features.

Quotes

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Key Insights Distilled From

Dealing with Missing Modalities in Multimodal Recommendation

by Daniele Mali... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19841.pdf

Deeper Inquiries

How can the proposed FeatProp approach be extended to handle other types of missing information in multimodal recommendation, such as missing user features or missing user-item interactions

The FeatProp approach can be extended to handle other types of missing information in multimodal recommendation by adapting the graph representation learning techniques to incorporate different types of missing data. For instance, to address missing user features, the FeatProp algorithm can be modified to propagate features not only among items but also among users in the user-item bipartite graph. By considering the interactions between users and items, the algorithm can impute missing user features based on the available information from connected items. Similarly, for missing user-item interactions, the FeatProp algorithm can be adjusted to propagate information between user and item nodes to infer potential interactions that are missing in the dataset. This adaptation would involve redefining the graph structure and feature propagation mechanisms to account for the specific characteristics of the missing data types.

How can the performance of FeatProp be further improved by incorporating additional information, such as item metadata or external knowledge graphs

To further improve the performance of FeatProp, additional information such as item metadata or external knowledge graphs can be integrated into the feature propagation process. By incorporating item metadata, FeatProp can leverage textual descriptions, categorical information, or other attributes associated with items to enhance the imputation of missing multimodal features. This additional data can provide valuable context and semantic information that can guide the feature propagation algorithm towards more accurate predictions. Furthermore, by incorporating external knowledge graphs, FeatProp can access a broader range of domain-specific knowledge to enrich the feature imputation process. External knowledge graphs can provide supplementary information about item relationships, attributes, or user preferences, which can be leveraged to improve the quality of recommendations generated by the multimodal recommender system.

What are the potential implications of the missing modalities problem in real-world multimodal recommendation scenarios, and how can the proposed solution be adapted to address practical challenges in industrial settings

The missing modalities problem in real-world multimodal recommendation scenarios can have significant implications for the accuracy and relevance of the recommendations provided to users. In practical industrial settings, where data quality and completeness are crucial for effective recommendation systems, the presence of missing modalities can lead to suboptimal performance and user dissatisfaction. To adapt the proposed FeatProp solution to address practical challenges in industrial settings, several strategies can be employed. Firstly, data preprocessing techniques can be implemented to handle missing modalities more effectively, such as data imputation methods tailored to the specific characteristics of the multimodal features. Additionally, the FeatProp algorithm can be optimized for scalability and efficiency to accommodate large-scale datasets commonly encountered in industrial applications. Moreover, incorporating domain-specific knowledge and expert insights into the feature propagation process can enhance the robustness and accuracy of the imputation process, leading to more reliable recommendations in real-world scenarios. By fine-tuning the FeatProp approach to suit the unique requirements and constraints of industrial settings, the missing modalities problem can be effectively mitigated, improving the overall performance and usability of multimodal recommendation systems.