SeRel-LightFM: Bridging Semantic and Relational Representations for Sparse Hybrid Recommendation

Abstract

Data sparsity remains a major obstacle for recommender systems because conventional collaborative filtering methods rely heavily on observed user–item interactions and often fail to exploit richer semantic and relational signals. To address this limitation, we propose SeRel-LightFM, a knowledge-aware hybrid recommendation framework built on top of LightFM. Our method combines two complementary feature-construction branches. First, we encode multi-field movie text with a Sentence-Transformer encoder and convert the resulting dense text embeddings into sparse semantic prototype features through K-Means clustering and soft assignment. Second, we construct a unified knowledge graph over users, movies, and metadata entities, learn joint embeddings with TransE, and transform these dense relational representations into sparse relational prototype features for both items and users. These newly constructed features, referred to as Advanced Relational and Semantic Representations (ARSR), are integrated with the original dataset-derived Conventional Feature Space (CFS) inside LightFM. Experiments on the multi-source CSP movie dataset under a leave-one-out evaluation protocol show that SeRel-LightFM consistently outperforms popularity, ItemKNN, and the LightFM baseline across Recall@K and NDCG@K at K = 5, 10, 20, and 50. The results indicate that combining semantic and relational prototype features improves ranking quality in sparse recommendation settings. The source code and dataset are available at https://github.com/gminh715/SeRel-LightFM.