A Trust-enhanced Recommender System for Patient-Doctor Matchmaking in Online Healthcare Communities

Abstract

In this era of vast online healthcare information, patients often find it challenging to choose the most appropriate doctors from a myriad of options available on different communities. The sheer volume of choices can be overwhelming, underscoring the importance of personalized doctor recommendations. Unfortunately, many existing communities rely on generic doctor recommendations through a global ranking system, potentially neglecting the unique needs and preferences of individual patients. To address these challenges, this study introduces a novel multicriteria user-item trust-enhanced collaborative filtering (MCUITeCF) approach, specifically designed to facilitate patients in locating doctors who match their unique preferences. This proposed approach capitalizes on multi-criteria ratings and integrates user-item trust relationships, aiming to enhance the quality of recommendations, while tackling the common issues of data sparsity and the cold-start problem. An examination of the proposed approach, using a realworld healthcare multi-criteria dataset, the RateMDs dataset, reveals its effectiveness in overcoming challenges associated with data sparsity and cold-start problems. The results demonstrate improved prediction accuracy and coverage compared to benchmark approaches, namely MC user-based CF, MC item-based CF, MC semantic-based CF, MC trust-based CF, and trust-semantic enhanced MC CF. Specifically, the results indicate that the MCUITeCF approach improves the average MAE by 66% compared to all of the benchmark approaches when tested on the RateMDs dataset. When dealing with data sparsity, the MCUITeCF approach improves the average MAE by 41% and prediction coverage by 23% compared to all of the benchmark approaches. In scenarios involving cold-start items, the MCUITeCF outperformed specific benchmark methods such as MC item-based CF, MC semantic-based CF, and Trust-Semantic enhanced MC CF, registering a 21% drop in average MAE and a 28% rise in prediction coverage. Similarly, for cold-start user situations, MCUITeCF excelled by decreasing the average MAE by 29% and a substantial increase in prediction coverage by 20% compared to MC user-based CF, MC trust-based CF, and trust-semantic enhanced MC CF benchmark approaches.