arXiv subjects

Kai Sugahara

Publications and source records attributed to Kai Sugahara.

3 recordsLinked to original sources

arXiv2025

Function-based Labels for Complementary Recommendation: Definition, Annotation, and LLM-as-a-Judge

Complementary recommendations enhance the user experience by suggesting items that are frequently purchased together while serving different functions from the query item. Inferring or evaluating whether two items have a complementary relationship requires complementary relationship labels; however, defining these labels is challenging because of the inherent ambiguity of such relationships. Complementary labels based on user historical behavior logs attempt to capture these relationships, but often produce inconsistent and unreliable results. Recent efforts have introduced large language models (LLMs) to infer these relationships. However, these approaches provide a binary classification without a nuanced understanding of complementary relationships. In this study, we address these challenges by introducing Function-Based Labels (FBLs), a novel definition of complementary relationships independent of user purchase logs and the opaque decision processes of LLMs. We constructed a human-annotated FBLs dataset comprising 2,759 item pairs and demonstrated that it covered possible item relationships and minimized ambiguity. We then evaluated whether some machine learning (ML) methods using annotated FBLs could accurately infer labels for unseen item pairs, and whether LLM-generated complementary labels align with human perception. Our results demonstrate that even with limited data, ML models, such as logistic regression and SVM achieve high macro-F1 scores (approximately 0.82). Furthermore, LLMs, such as gpt-4o-mini, demonstrated high consistency (0.989) and classification accuracy (0.849) under the detailed definition of FBLs, indicating their potential as effective annotators that mimic human judgment. Overall, our study presents FBLs as a clear definition of complementary relationships, enabling more accurate inferences and automated labeling of complementary recommendations.

cs.IR↗

arXiv2025

Knowledge-Augmented Relation Learning for Complementary Recommendation with Large Language Models

Complementary recommendations play a crucial role in e-commerce by enhancing user experience through suggestions of compatible items. Accurate classification of complementary item relationships requires reliable labels, but their creation presents a dilemma. Behavior-based labels are widely used because they can be easily generated from interaction logs; however, they often contain significant noise and lack reliability. While function-based labels (FBLs) provide high-quality definitions of complementary relationships by carefully articulating them based on item functions, their reliance on costly manual annotation severely limits a model's ability to generalize to diverse items. To resolve this trade-off, we propose Knowledge-Augmented Relation Learning (KARL), a framework that strategically fuses active learning with large language models (LLMs). KARL efficiently expands a high-quality FBL dataset at a low cost by selectively sampling data points that the classifier finds the most difficult and uses the label extension of the LLM. Our experiments showed that in out-of-distribution (OOD) settings, an unexplored item feature space, KARL improved the baseline accuracy by up to 37%. In contrast, in in-distribution (ID) settings, the learned item feature space, the improvement was less than 0.5%, with prolonged learning could degrade accuracy. These contrasting results are due to the data diversity driven by KARL's knowledge expansion, suggesting the need for a dynamic sampling strategy that adjusts diversity based on the prediction context (ID or OOD).

cs.IR↗

arXiv2024

Hierarchical Matrix Factorization for Interpretable Collaborative Filtering

Matrix factorization (MF) is a simple collaborative filtering technique that achieves superior recommendation accuracy by decomposing the user-item interaction matrix into user and item latent matrices. Because the model typically learns each interaction independently, it may overlook the underlying shared dependencies between users and items, resulting in less stable and interpretable recommendations. Based on these insights, we propose "Hierarchical Matrix Factorization" (HMF), which incorporates clustering concepts to capture the hierarchy, where leaf nodes and other nodes correspond to users/items and clusters, respectively. Central to our approach, called hierarchical embeddings, is the additional decomposition of the latent matrices (embeddings) into probabilistic connection matrices, which link the hierarchy, and a root cluster latent matrix. The embeddings are differentiable, allowing simultaneous learning of interactions and clustering using a single gradient descent method. Furthermore, the obtained cluster-specific interactions naturally summarize user-item interactions and provide interpretability. Experimental results on ratings and ranking predictions show that HMF outperforms existing MF methods, in particular achieving a 1.37 point improvement in RMSE for sparse interactions. Additionally, it was confirmed that the clustering integration of HMF has the potential for faster learning convergence and mitigation of overfitting compared to MF, and also provides interpretability through a cluster-centered case study.

cs.IR↗