Ordinal Embedding for Collaborative Filtering: A Unified Regularization for Enhanced Generalization and Interpretability

Collaborative filtering is a primary paradigm of modern recommender systems. A typical practice is to embed collaborative signals into a latent space and infer recommendation scores based on the similarities between user and item embeddings. Besides inter-type similarities (i.e., user-item relationships), intra-type similarities (i.e., user-user, and item-item) are also essential as they capture the intrinsic structure of users and items. However, many existing recommendation models only learn inter-type similarities using objectives like ranking loss or binary classification loss, while neglecting intra-type similarities. Consequently, the intrinsic structures of users and items are often distorted in the latent space, where users with similar historical interactions diverge more than those dissimilar. In this study, we show the importance of preserving the ordinal relations of intra-type similarities. We provide a theoretical analysis suggesting that preserving intra-type similarity rankings can enhance a model's generalizability and interpretability. In addition, we propose a regularization that enforces a constraint on the rankings of intra-type similarities, ensuring that learning inter-type similarities does not break intrinsic ordinal structures. It can be seamlessly integrated into most latent factor models and can be jointly trained with their original objectives. Extensive experiments on 4 benchmark datasets and 5 representative models show that our ordinal regularization can consistently improve recommendation performance, and enhance the intra-type similarity coherence in the latent space. The results also exhibit enhanced generalizability and interpretability of recommendations.

• Publication year

  2025

• Venue

  International Conference on Information and Knowledge Management

• Publication date

  2025-11-10

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1145/3746252.3760992
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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