Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Cao, Buqing; Li, Bing; Liu, Jianxun; Tang, Mingdong; Liu, Yizhi; Li, Yanxinwen

doi:10.1155/2019/6423805

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…By levering the Wikipedia corpus, Cao et al. [14] enriched mashups and services in content and then fused features, including the extracted content, similar services or mashups, popularity, and the co‐occurrence of services, into an FM to capture their second‐order interaction. Furthermore, Cao et al.…”

Section: Related Workmentioning

confidence: 99%

“…The CF-based approaches [5][6][7][8] leverage the historical experience of similar mashups/services to generate a recommendation list. By integrating content-based and CF-based approaches, the hybrid approaches [9][10][11][12][13][14][15] consider explicitly specified requirements, implicit invocation preferences, and other information of service usages, such as co-invocation and popularity, to make recommendations.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Geng

Wang

et al. 2022

CAAI Trans on Intel Tech

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Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service-based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service-oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single-round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR can outperform several state-of-the-art service recommendation methods to develop new mashups iteratively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Geng

Wang

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…Most works use the dataset from Pro-grammableWeb, and they exploit different kinds of auxiliary information, including functional descriptions, tags, categories, providers, architectural styles, etc., as the mashupservice composition record is extremely sparse. Based on the modeling of the mashup-service composition record, existing research can be roughly divided into three categories: neighbor-based collaborative filtering (CF) methods [11][12][13][14][15], latent factor-based CF methods [6,[16][17][18][19][20][21] and deep learning-based methods [8,9,[22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

“…In [19], Li et al integrate tag, topic, co-occurrence, and popularity factors in the FM for service recommendation, where they exploit the enriched tags and topics of mashups and services derived by RTM and use the invocation times and category information of services to derive their popularity. In [20], Cao et al extend the description of services using Word2vec and derive latent topics by the hierarchical dirichlet process (HDP). FM is then applied to train these latent topics for service recommendation.…”

Section: Related Workmentioning

confidence: 99%

Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

Gao

2022

Mathematical Problems in Engineering

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With an increasing number of web services on the Web, selecting appropriate services to meet the developer’s needs for mashup development has become a difficult task. To tackle the problem, various service recommendation methods have been proposed. However, there are still challenges, including the sparsity and imbalance of features, as well as the cold-start of mashups and services. To tackle these challenges, in this paper, we propose a Multigraph Convolutional Network enhanced Neural Factorization Machine model (MGCN-NFM) for service recommendation. It first constructs three graphs, namely, the collaborative graph, the description graph, and the tag graph. Each graph represents a different type of relation between mashups and services. Next, graph convolution is performed on the three graphs to learn the feature embeddings of mashups, services, and tags. Each node iteratively aggregates the information from its higher-order neighbors through message passing in each graph. Finally, the feature embeddings as well as the description features learned by Doc2vec are modeled by the neural factorization machine model, which captures the nonlinear and higher-order feature interaction relations between them. We conduct extensive experiments on the ProgrammableWeb dataset, and demonstrate that our proposed method outperforms state-of-the-art factorization machine-based methods in service recommendation.

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“…Depending on these pseudo ratings, the authors present a web service recommendation approach, which can reduce the cold-start problem by integrating contextual information (e.g., time of updating web services and user location) and an online learning model. In [37] both word embeddings enhanced the hierarchical process and factorization machines are integrated to recommend mobile services to build high-quality Mashup application. The proposed work extended the description documents of Mashup applications and mobile services by using Word2vec tool and derived latent topics from the extended description documents of Mashup and mobile services.…”

Section: Service Recommender Systemmentioning

confidence: 99%

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Al-Zanbouri¹

2021

Preprint

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Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

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Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Cited by 10 publications

References 23 publications

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

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