Cloud service recommendation based on unstructured textual information

Jiang, Yuanchun; Tao, Dandan; Liu, Yezheng; Sun, Jianshan; Ling, Haifeng

doi:10.1016/j.future.2019.02.063

Cited by 32 publications

(21 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in document [32], since the traditional image feature representation cannot capture product styles, highly related style feature modeling was introduced into the visual recommendation model and the style feature was integrated into collaborative learning to incorporate user preferences and improve the validity of the sensory recommendation method. In document [33], a Hierarchical Dirichlet Processes model was proposed based on service description text and service tag information. The personalized PageRank algorithm based on the service tags was used to rank and recommend cloud services in each cluster to alleviate the difficulty of personalized cloud services.…”

Section: Related Workmentioning

confidence: 99%

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Considering the low satisfaction rate and low efficiency of product service plans, a personalized product service plan recommendation method adopting the degree of trust and cloud model is proposed. The recommendation algorithm mainly includes calculating the similarity and the prediction results of the scheme. First, by fully considering the user's subjective characteristics, the user's trust is used to improve the traditional similarity. Second, considering data sparsity and discreteness, the cloud drop distance similarity calculation method is introduced in the process of calculating the trust similarity, and a new similarity is generated via the weighted synthesis to predict and fill in the gaps in the data. Then, when the new user does not have the cold start problem caused by the historical score record, the neural network method can be used to classify the users based on the user characteristics. The method is proposed and introduced to predict sparse user interest features and obtain similar user sets based on feature classification. The corresponding program offers recommendations. Finally, the effectiveness and rationality of the proposed method are verified by using the recommendations for a machine tool product for a manufacturing enterprise as an example. INDEX TERMS Sparsity, cold start, user trust, cloud drop distance similarity.

show abstract

Section: Related Workmentioning

confidence: 99%

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…e average amount of error expected to identify a service by using the proposed framework is 11% compared to 31% by using the cloud service discovery solution. Hierarchical Dirichlet processes (HDP) model and personalized Pag-eRank algorithm are used to achieve a two-stage model for cloud service recommendation by integrating the information of service descriptive texts and service tags [14]. Nabli proposes a self-adaptive semantic focused crawler based on latent Dirichlet allocation (LDA) for efficient cloud service discovery [15].…”

Section: Related Workmentioning

confidence: 99%

“…obtain a place p in τ (t n ) and build service net SubSN j with SubSN j .i � p; (11) sp j � PathString_Generate (SubSN j ); (12) if (I (t n ) � O ∨ ) PS � PS + sp j + ||; (13) if (I (t n ) � O ∧ ) PS � PS + sp j + ⊗; (14) End for (15) Scientific Programming [28], service class [29], and service cluster [26,30]. Cloud services in above concepts are required with the same input and output parameters.…”

Section: Service Substitution Based On Clustering and Process Collabomentioning

confidence: 99%

See 1 more Smart Citation

A Cluster and Process Collaboration-Aware Method to Achieve Service Substitution in Cloud Service Processes

Shen

2020

Scientific Programming

View full text Add to dashboard Cite

Some cloud services may be invalid since they are located in a dynamically changing network environment. Service substitution is necessary when a cloud service cannot be used. Existing work mainly concerned on service function and quality in service substitution. To select a more suitable substitutive service, process collaboration similarity needs to be considered. This paper proposes a cluster and process collaboration-aware method to achieve service substitution. To compute the process collaboration similarity, we use logic Petri nets to model service processes. All the service processes are transformed into path strings. Service vectors for cloud services are generated by Word2Vec from these path strings. Process collaboration similarity of two cloud services is obtained by computing the cosine value of their service vectors. Meanwhile, similar cloud services are classified as a service cluster. By calculating function similarity and quality matching, a candidate set for services substitution is generated. The service with the highest process collaboration similarity to invalid one in the candidate set is chosen as the substitutive one. Simulation experiments show the proposed method is less time-consuming than traditional methods in finding substitutive service. Meanwhile, the substitutive one has a high cooccurrence rate with neighboring services of the invalid cloud service. Thus, the proposed method is efficient and integrates process collaboration well in service substitution.

show abstract

“…According to the characteristics of a dataset, it is necessary to apply different mining techniques. Among them, text and opinion mining for the analysis of unstructured text data have attracted considerable attention [3]. Opinion mining is a technique whereby useful information is extracted by analyzing people opinions.…”

Section: Introductionmentioning

confidence: 99%

Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

2020

View full text Add to dashboard Cite

With the increase in traffic accident rates, traffic risk detection is becoming increasingly important. Moreover, it is necessary to provide appropriate traffic information considering user locations and routes and design an analysis method accordingly. This paper proposes a word-embedding-based traffic document classification model for detecting emerging risks using a quantity termed sentiment similarity weight (SSW). The proposed method detects emerging risks by considering and classifying the importance and polarity of keywords in traffic document. Conventional sentiment analysis methods fail to utilize semantically significant keywords unless they are included in a sentiment dictionary. In this study, through word imputation using an established similarity dictionary and by widening the limited utilization range, the proposed method overcomes the disadvantage of sentiment dictionaries. The proposed method is evaluated through three tests. In the first, the similarity between keywords is measured, and thus model accuracy is evaluated. In the second test, three classifiers for emerging risk classification are compared. In the last test, emerging risk detection is assessed according to whether the proposed SSW is applied, and its effectiveness is therefore verified. The evaluation results demonstrate that the proposed traffic-related document classification model using the SSW has an f-measure of 0.907, indicating satisfactory performance. Therefore, the proposed SSW can be effectively used as a parameter in traffic-related document classification and enables the detection of emerging risks.

show abstract

Cloud service recommendation based on unstructured textual information

Cited by 32 publications

References 17 publications

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

Personalized Product Service Scheme Recommendation Based on Trust and Cloud Model

A Cluster and Process Collaboration-Aware Method to Achieve Service Substitution in Cloud Service Processes

Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

Contact Info

Product

Resources

About