Web Service Recommendation Based on Word Embedding and Topic Model

Chen, Ting; Liu, Jianxun; Cao, Buqing; Peng, Zhenlian; Wen, Yiping

doi:10.1109/bdcloud.2018.00133

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…Functional characteristics-based Web services recommendation mainly explores the matching of functional similarity between user requirement and services, ranks and recommends services based on the degree of matching [3][4][5][6][7]. Among them, some researchers utilize the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm to extract the similarity between service texts (e.g., WSDL functional descriptions), and map the semantic information of service documents into a dense vector space.…”

Section: Functional Characteristics Based Web Services Recommendationmentioning

confidence: 99%

“…These mainly include the methods based on functional features, non-functional features, and hybrid features. Functional-based service recommendation focuses on the matching of functional similarity between user requirements and services, ranking and recommending services based on the degree of matching [3][4][5][6][7]. Researchers have adopted various approaches to achieve this goal.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers use the TF-IDF algorithm to mine the similarity between service texts (e.g., WSDL functional descriptions) and map the functional semantic information of services into a dense vector space [3,4]. Others have attempted to improve the accuracy of service similarity matching by using document topic generation models to improve recommendation performance [5][6][7]. However, these methods are difficult to obtain high-quality recommendation, with facing some challenges such as labour-intensive feature engineering and unstable performance.…”

Section: Introductionmentioning

confidence: 99%

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ARIR: an intent recognition-based approach for API recommendation

Cao,

Wang,

Xie

et al. 2024

Cluster Comput

Self Cite

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With the prevalence of service composition, how to recommend API services that meet the Mashup requirements for developers has become a challenging in the field of service computing. Existing works on Web service recommendation typically conduct service recommendation based on the semantic requirements and interaction relationships of Web services, while ignoring the construction intent of Mashup creators. Moreover, they often only focus on the impact of single intent on recommendation, neglecting the diverse requirements of Mashup creators at different intent-levels. As a result, the recommendation models fail to comprehensively understand the construction intent of Mashup creators, which affects the quality and effectiveness of Web service recommendation. To address this problem, this paper proposes an intent recognition-based API recommendation method, denoted as ARIR. This method utilizes the annotation information of Mashup and API to analyse the creation intent of Mashup and the functional intent of API, thereby providing more accurate API with high-quality to Mashup creators. Firstly, it decouples the representations of Mashup and API at different intentlevels and independently initializes the node representations at each intent-level. Secondly, it trains the vector representations at each intent-level using a decoupled graph convolutional neural network module and obtains the representation vectors of Mashup/API at different intent-levels with attention weights. Then, it aggregates the intents of Mashup and API using the Mashup-API interaction relationships, resulting in the final node representations of Mashup/API. Furthermore, it constructs a similarity heterogeneous network by calculating the Mashup-to-Mashup similarity and API-to-API similarity, updating the node representations by using the Mashup and API feature matrices and adjacency matrices, and obtaining the final recommendation prediction results by using a fully connected layer. Finally, the experimental results conducted on real-world Web service datasets demonstrate that the ARIR outperforms the best-performing baseline method with Recall@20 of 3% and NDCG@20 of 5%.

show abstract

Section: Functional Characteristics Based Web Services Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ARIR: an intent recognition-based approach for API recommendation

Cao,

Wang,

Xie

et al. 2024

Cluster Comput

Self Cite

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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 [22], Zhang et al cluster the descriptions of services using Doc2Vec and use the DeepFM model [30] to mine the higher-order composition relations. In [23], Chen et al also adopt the DeepFM model by taking the features learned from word embedding and the Dirichlet mixture model (DMM) as input. In [8], Cao et al propose an attention FM [31] by employing an attention mechanism that learns the importance of each input feature interaction via a neural network model.…”

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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Web services recommendation based on Metapath-guided graph attention network

Zhang

Wang

et al. 2022

J Supercomput

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Web Service Recommendation Based on Word Embedding and Topic Model

Cited by 10 publications

References 11 publications

ARIR: an intent recognition-based approach for API recommendation

ARIR: an intent recognition-based approach for API recommendation

Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

Web services recommendation based on Metapath-guided graph attention network

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