Journal ArticleDOI
Unified Collaborative and Content-Based Web Service Recommendation
Reads0
Chats0
TLDR
This paper proposes a novel approach that unifies collaborative filtering and content-based recommendation of web services using a probabilistic generative model, which outperforms the state-of-the-art methods on recommendation performance.Abstract:
The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With increasing adoption and presence of web services, designing novel approaches for efficient and effective web service recommendation has become of paramount importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for a system to recommend web services that align with users’ interests without requiring the users to explicitly specify queries. Recent research efforts on web service recommendation center on two prominent approaches: collaborative filtering and content-based recommendation . Unfortunately, both approaches have some drawbacks, which restrict their applicability in web service recommendation. In this paper, we propose a novel approach that unifies collaborative filtering and content-based recommendations. In particular, our approach considers simultaneously both rating data (e.g., QoS) and semantic content data (e.g., functionalities) of web services using a probabilistic generative model. In our model, unobservable user preferences are represented by introducing a set of latent variables, which can be statistically estimated. To verify the proposed approach, we conduct experiments using 3,693 real-world web services. The experimental results show that our approach outperforms the state-of-the-art methods on recommendation performance.read more
Citations
More filters
Journal ArticleDOI
Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios
TL;DR: A novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-me means) called TCCF is proposed, which can provide a higher quality recommendation by analyzing the user's behaviors and cluster similar users together for further quick and accurate recommendation.
Journal ArticleDOI
A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining
TL;DR: The proposed hybrid approach can alleviate both the cold-start and data sparsity problems by making use of ontological domain knowledge and learner’s sequential access pattern respectively before the initial data to work on is available in the recommender system.
Journal ArticleDOI
A Survey of Collaborative Filtering-Based Recommender Systems for Mobile Internet Applications
TL;DR: A framework of the CF recommender system based on various user data including user ratings and user behaviors is proposed, and several typical CF algorithms are classified as memory-based approaches and model- based approaches and compared.
Journal ArticleDOI
Context-aware QoS prediction for web service recommendation and selection
TL;DR: This paper proposes two novel prediction models, which are capable of using the context information of users and services respectively, and proposes an ensemble model to combine the results of the two models.
Journal ArticleDOI
A hybrid recommender system for e-learning based on context awareness and sequential pattern mining
TL;DR: Evaluation of the proposed hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Posted Content
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
TL;DR: In this article, the authors compare the predictive accuracy of various methods in a set of representative problem domains, including correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods.