scholarly journals Collaborative Filtering Method of Web Service Recommendation Based on Location Aware and Personalized

CONVERTER ◽  
2021 ◽  
pp. 583-589
Author(s):  
Li Ziman
Keyword(s):  
Web Services ◽  
Web Service ◽  
Autonomous System ◽  
Recommendation System ◽  
Service Recommendation ◽  
Filtering Method ◽  
Location Aware ◽  
The Face ◽  
Location Clustering ◽  
User Location

With the rapid growth of the number of Web services, it is necessary to build an efficient web service recommendation system in the face of massive web services. In order to recommend high-quality services to users, the key problem is how to obtain the s value of Web services. This paper proposes a collaborative web service recommendation method based on location clustering. Firstly, users are clustered according to the autonomous system by using the correlation between QoS and user location. According to the clustering results, the system fills in the vacancy Qos value; Then, the vacancy Qos value is filled in in advance and the similarity between active users and each user is calculated. Based on this, to P-K algorithm is used to obtain the most similar Qos value to predict the unknown service for active users to complete the recommendation. The method proposed in this paper can effectively solve the problem of data sparsity and cold start of Web services. At the same time, a better balance between accuracy and coverage is obtained.

scholarly journals Service Recommendation with High Accuracy and Diversity

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shengqi Wu ◽  
Huaizhen Kou ◽  
Chao Lv ◽  
Wanli Huang ◽  
Lianyong Qi ◽  
...  
Keyword(s):  
Web Services ◽  
Web Service ◽  
Weight Vector ◽  
High Accuracy ◽  
Information Resources ◽  
Cosine Similarity ◽  
Service Recommendation ◽  
Key Factor ◽  
Novel Method

In recent years, the number of web services grows explosively. With a large amount of information resources, it is difficult for users to quickly find the services they need. Thus, the design of an effective web service recommendation method has become the key factor to satisfy the requirements of users. However, traditional recommendation methods often tend to pay more attention to the accuracy of the results but ignore the diversity, which may lead to redundancy and overfitting, thus reducing the satisfaction of users. Considering these drawbacks, a novel method called DivMTID is proposed to improve the effectiveness by achieving accurate and diversified recommendations. First, we utilize users’ historical scores of web services to explore the users’ preferences. And we use the TF-IDF algorithm to calculate the weight vector of each web service. Second, we utilize cosine similarity to calculate the similarity between candidate web services and historical web services and we also forecast the ranking scores of candidate web services. At last, a diversification method is used to generate the top- K recommended list for users. And through a case study, we show that DivMTID is an effective, accurate, and diversified web service recommendation method.

QoS Prediction for Web Service Recommendation with Network Location-Aware Neighbor Selection

2016 ◽  
Vol 26 (04) ◽  
pp. 611-632 ◽  
Author(s):  
Yuyu Yin ◽  
Song Aihua ◽  
Gao Min ◽  
Xu Yueshen ◽  
Wang Shuoping
Keyword(s):  
Web Service ◽  
Real World ◽  
Missing Values ◽  
Prediction Models ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Network Location ◽  
Collaborative Recommendation ◽  
Location Aware ◽  
Neighbor Selection

Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user’s demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters.

Location-aware and personalized collaborative filtering for web service recommendation

2016 ◽  
Vol 9 (5) ◽  
pp. 686-699 ◽  
Author(s):  
Jianxun Liu ◽  
Mingdong Tang ◽  
Zibin Zheng ◽  
Xiaoqing Liu ◽  
Saixia Lyu
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Service Recommendation ◽  
Location Aware

scholarly journals An effective method for clustering-based web service recommendation

2022 ◽  
Vol 12 (2) ◽  
pp. 1571
Author(s):  
Ha Huy Cuong Nguyen ◽  
Bui Thanh Khiet ◽  
Van Loi Nguyen ◽  
Thanh Thuy Nguyen
Keyword(s):  
Web Services ◽  
Web Service ◽  
Performance Metrics ◽  
Research Problem ◽  
Quality Of Services ◽  
The Novel ◽  
Service Recommendation ◽  
Functional Relevance ◽  
Web Service Classification

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.

scholarly journals Qos Web Service Design Based on Collaborative Filtering

CONVERTER ◽  
2021 ◽  
pp. 107-115
Author(s):  
Yu-ping LI, Ke LI, Zhan-jie Guo
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Service Recommendation ◽  
Recommendation Algorithm ◽  
Similar Weight ◽  
Definition Of ◽  
Collaborative Prediction

In the process of web service recommendation, the prediction accuracy of Web Service missing Quality of Service (QoS) value will have an important impact on the rationality of service recommendation. Therefore, combined with spatiotemporal similarity perception, this paper proposes a new web service QoS collaborative filtering recommendation algorithm. This paper designs the framework of web service recommendation system from the perspective of QoS collaborative prediction, and gives the definition of related parameter set. Aiming at the problem that some services in the traditional Top-k algorithm are not similar to the target services, the spatial-temporal similarity perception combined with similar weight is used to predict the missing data to improve the prediction accuracy. In this paper, the calculation process of the algorithm is given through a simple example. The effectiveness of the algorithm is verified by the experimental results.

On Personalizing Web Services Using Context

2011 ◽  
pp. 232-253 ◽  
Author(s):  
Zakaria Maamar ◽  
Soraya Kouadri Mostéfaoui ◽  
Qusay H. Mahmoud
Keyword(s):  
Web Services ◽  
Web Service ◽  
User Preferences ◽  
Temporal Perspective ◽  
User Context ◽  
Different Types ◽  
User Location ◽  
The Web

This chapter presents a context-based approach for Web services personalization so that user preferences are accommodated. Preferences are of different types, varying from when the execution of a Web service should start to where the outcome of this execution should be delivered according to user location. Besides user preferences, it will be discussed in this chapter that the computing resources on which the Web services operate have an impact on their personalization. Indeed, resources schedule the execution requests that originate from multiple Web services. To track the personalization of a Web service from a temporal perspective (i.e., what did happen, what is happening, and what will happen), three types of contexts are devised and referred to as user context, Web service context, and resource context.

Location-Based Collaborative Filtering for Web Service Recommendation

2019 ◽  
Vol 12 (1) ◽  
pp. 34-40
Author(s):  
Mareeswari Venkatachalaappaswamy ◽  
Vijayan Ramaraj ◽  
Saranya Ravichandran
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Information Filtering ◽  
Service Selection ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Service Recommendation ◽  
Location Aware

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.

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