Combining Social Balance Theory and Collaborative Filtering for Service Recommendation in Sparse Environment

With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services, through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, the CF-based recommendation approaches can work well, when the target user has similar friends or the target services (i.e., the services preferred by target user) have similar services. However, in certain situations when user-service rating data is sparse, it is possible that target user has no similar friends and target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result, which brings a great challenge for accurate service recommendation. In view of this challenge, we combine Social Balance Theory (i.e., SBT) and CF to put forward a novel recommendation approach Rec(SBT+CF). Finally, the feasibility of our proposal is validated, through a set of simulation experiments deployed on MovieLens-1M dataset.