Deep hybrid collaborative filtering for Web service recommendation
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Summary
A novel deep learning based hybrid approach for Web service recommendation by combining collaborative filtering and textual content is proposed, which can achieve better recommendation performance than several state-of-the-art methods.
- Type
- article
- Published
- 2018-11-01
- Cited by
- 139
- References
- 34
- OpenAlex
- https://openalex.org/W2807511999
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:51888328
Keywords
Computer science, Collaborative filtering, Mashup, Web service, Recommender system
References
- Integrating implicit feedbacks for time-aware web service recommendations
- Service Recommendation for Mashup Composition with Implicit Correlation Regularization
- An Integrated Semantic Web Service Discovery and Composition Framework
- Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems
- Category-Aware API Clustering and Distributed Recommendation for Automatic Mashup Creation
- WSMO-Lite and hRESTS: Lightweight semantic annotations for Web services and RESTful APIs
- KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications
- Constructing a Global Social Service Network for Better Quality of Web Service Discovery
- Short Text Similarity with Word Embeddings
- Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization
- Semantics-Based Automated Service Discovery
- A Social-Aware Service Recommendation Approach for Mashup Creation
- Mashup Service Recommendation Based on User Interest and Social Network
- Collaborative Filtering for Implicit Feedback Datasets
- Recommending Web Services via Combining Collaborative Filtering with Content-Based Features
- A Discriminative Kernel-Based Approach to Rank Images from Text Queries
- GloVe: Global Vectors for Word Representation
- Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
- Wide & Deep Learning for Recommender Systems
- Joint Modeling Users, Services, Mashups, and Topics for Service Recommendation
Cited by
- A multi-objective service composition recommendation method for individualized customer: Hybrid MPA-GSO-DNN model
- Cloud Service Composition with Multiple QoS Constraints for Manufacturing Resource
- Evaluating Collaborative Filtering Recommender Algorithms: A Survey
- Paragraph-based complex networks: application to document classification and authenticity verification
- Leveraging contextual information for cold‐start Web service recommendation
- A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing
- DUSKG: A fine-grained knowledge graph for effective personalized service recommendation
- Impact of Feature selection on content-based recommendation system
- Classification of web services using data mining algorithms and improved learning model
- RecDNNing: a recommender system using deep neural network with user and item embeddings
- Location-Aware Deep Collaborative Filtering for Service Recommendation
- Generative Adversarial Network Based Service Recommendation in Heterogeneous Information Networks
- Recurrent Tensor Factorization for time-aware service recommendation
- Clustering Services Based on Community Detection in Service Networks
- A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation
- Song Recommendation System Using Collaborative Filtering Methods
- Group recommendation based on hybrid trust metric
- A probability distribution detection based hybrid ensemble QoS prediction approach
- Discovering web services in social web service repositories using deep variational autoencoders
- Recurrent Neural Network for Web Services Performance Forecasting, Ranking and Regression Testing
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