Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

The number of mobile application services is showing an explosive growth trend, which makes it difficult for users to determine which ones are of interest. Especially, the new mobile application services are emerge continuously, most of them have not be rated when they need to be recommended to users. This is the typical problem of cold start in the field of collaborative filtering recommendation. This problem may makes it difficult for users to locate and acquire the services that they actually want, and the accuracy and novelty of service recommendations are also difficult to satisfy users. To solve this problem, a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper. First, the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation, part-of-speech tagging, and dependency parsing. It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity. Then, a language representation model called Bidirectional Encoder Representation from Transformers (BERT) is used to vectorize the content feature text, and an improved weighted word mover's distance algorithm based on Term Frequency-Inverse Document Frequency (TFIDF-WMD) is used to calculate the similarity of mobile application services. Finally, the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm. The experimental results show that by using the proposed hybrid recommendation method presented in this paper, the cold start problem is alleviated to a certain extent, and the accuracy of the recommendation result has been significantly improved.