Service Recommendation with High Accuracy and Diversity

In recent years, the number of web services grows explosively. With a large amount of information resources, it is difficult for users to quickly find the services they need. Thus, the design of an effective web service recommendation method has become the key factor to satisfy the requirements of users. However, traditional recommendation methods often tend to pay more attention to the accuracy of the results but ignore the diversity, which may lead to redundancy and overfitting, thus reducing the satisfaction of users. Considering these drawbacks, a novel method called DivMTID is proposed to improve the effectiveness by achieving accurate and diversified recommendations. First, we utilize users’ historical scores of web services to explore the users’ preferences. And we use the TF-IDF algorithm to calculate the weight vector of each web service. Second, we utilize cosine similarity to calculate the similarity between candidate web services and historical web services and we also forecast the ranking scores of candidate web services. At last, a diversification method is used to generate the top-K recommended list for users. And through a case study, we show that DivMTID is an effective, accurate, and diversified web service recommendation method.