Paramate Phuengtrakul, Jakramate Bootkrajang, and Dussadee Praserttipong
Published in Data Science and Engineering (DSE) Record 2026 Vol. 7 No. 1 pp. 82-98
Abstract
Trust-aware recommender systems leverage social trust to mitigate rating sparsity and the cold-start problem, yet most public trust datasets represent trust as sparse binary links, which can underutilize structural information in the trust network. This paper proposes a Katz-based trust enrichment method that transforms binary directed trust into continuous multi-hop trust signals — capturing friend-of-friend propagation via truncated path counting — and integrates these signals into TrustSVD to improve recommendation accuracy while maintaining a practical accuracy–complexity trade-off. The proposed method is instantiated as four variants: Katz-2, Katz-3, and their corresponding boosted variants (Boosted Katz-2 and Boosted Katz-3), which differ in propagation depth and whether direct trust edges are re-emphasized after propagation. To characterize the value of multi-hop propagation, the proposed method is evaluated against three reference representations: the binary baseline used by the original TrustSVD and two local-overlap benchmarks (cosine and Jaccard similarity) that capture only direct neighborhood agreement without path propagation. Using FilmTrust and CiaoDVD, the study evaluates all seven trust representations under a unified training and hyperparameter tuning protocol, with performance reported via 5-fold cross-validation on RMSE and MAE for both all-user and cold-start user groups (rating < 10 and rating < 5). Results show that the proposed Katz-based method yields modest but consistent accuracy improvements over the binary baseline, with the clearest benefits in cold-start settings and in the sparser CiaoDVD dataset. Across settings, Katz-2 emerges as the most reliable variant of the proposed method, whereas the most extreme coldstart group in CiaoDVD (rating < 5) slightly favors the Jaccard benchmark. Given that training cost is dominated by repeated SGD updates and increases with enlarged effective trust neighborhoods, Katz-2 offers a strong default balance between accuracy gains and computational overhead.