A trust-semantic fusion-based recommendation approach for e-business applications

Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsity, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust–Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach.

► A trust–semantic fusion-based recommendation approach is proposed for B2B e-service.
► It fuses user-based trust-enhanced CF and item-based semantic-enhanced CF.
► It utilizes trust intuitive property to reduce the effort of cold-start user problems.
► It uses item semantic relationship to reduce the effect of cold-start item problem.
► It can alleviate data sparsity problem.