Unified collaborative and content-based web service recommendation

Lina Yao, Quan Z. Sheng, Anne H H Ngu, Jian Yu, Aviv Segev

Research output: Contribution to journalArticlepeer-review

129 Citations (Scopus)

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.

Original languageEnglish
Article number6894179
Pages (from-to)453-466
Number of pages14
JournalIEEE Transactions on Services Computing
Volume8
Issue number3
DOIs
Publication statusPublished - 1 May 2015
Externally publishedYes

Keywords

  • collaborative filtering
  • content-based recommendation
  • data sparsity
  • hybrid approach
  • service discovery
  • three-way aspect model
  • Web service recommendation

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