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

a r t i c l e i n f o Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsi-ty, 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. Recommender systems are considered the most popular forms of web personalization and have become a promising and important research topic in information sciences and decision support systems Recommender systems are used to either predict whether a particular user will like a particular item or to identify a set of k items that will be of interest to a certain user, and have been used in different web-based applications including e-business, e-learning and e-tourism [8,22,31]. Currently, Collaborative Filtering (CF) is probably the most known and commonly used recommendation approach in recommender systems. CF works by collecting user ratings for items in a given domain and computing similarities between users or between items in order to produce recommendations [1,31]. CF can be further divided into user-based and item-based CF approaches. In user-based CF approach, a user will receive recommendations of items that similar users liked. In item-based CF approach, a user will receive recommendations of items that are similar to the ones that the user liked in the past [1]. Despite their popularity and success, the CF-based approaches still suffer from some major limitations ; these include data sparsity, cold-start user and cold-start item problems [1,3,36,37]. The data sparsity problem occurs when the number of available items increases and the number of ratings in the rating matrix is insufficient for generating accurate predictions. When the ratings obtained are very small compared to the number of ratings that are needed to be predicted, a recommender system becomes unable to locate similar neighbors and produces poor recommendations. The cold-start (CS) user problem, which is also known as the new user problem, affects users who have none, or a small number of ratings. When the number of rated items is small for the CS user, …