Service Recommendation with High Accuracy and Diversity
Table 2
The basic framework of DivMTID.
Step 1: explore users’ preferences approximately
By establishing the relationship between a user’s history score records and the information of the metadata module, the preference degree of each module is calculated, and the user’s preferences is approximately explored.
Step 2: calculate TF-IDF weight vectors of web services
Using the TF-IDF algorithm, the importance of words in the corpus to web services is calculated and finally represented by the TF-IDF weight vector in order to make a distinction among web services.
Step 3: predict the ranking scores of candidate services
The similarity between candidate web services and historically used web services is calculated by using cosine similarity, and the ranking score values of candidate web services are predicted.
Step 4: create a diversified web service recommended list
According to different index numbers, different web services are selected to form multiple recommended lists. Then, it needs to calculate the list-diversity value of each list, and the list with the highest value becomes the web service recommended list that is finally recommended to the user.
