Unified Collaborative and Content-Based Web Service Recommendation

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.

Web service recommendation based on time‐aware users clustering and multi‐valued QoS prediction

“…Approaches on time‐aware service recommendation can be categorized depending on the target recommendation time. We distinguish continuous time‐aware approaches, categorical time‐aware approaches, and time‐adaptive approaches . These three categories are described in the following sections.…”

A Space-Time Periodic Task Model for Recommendation of Remote Sensing Images

“…However, this approach performs poorly if the user group size is small or the historical rating data are sparse, which is the well-known cold start problem. Recent studies [25][26][27][28] have demonstrated that a hybrid approach, combining CF and content-based approaches can provide more accurate recommendations and solve some of the common problems in recommendation systems such as cold start and the sparsity problem. Even so, it does not perform recommendation based on spatial data.…”

Web Service Ranking using Rank Aggregation Method

“…H. H. Ngu, Jian Yu, and Aviv Segev (2014), Presented Unified Collaborative and Content-Based Web Service Recommendation [10]. The author proposes a novel hybrid approach that consolidates collaborative filtering and semantic content-based methods for a recommendation of services.…”