Service Recommendation Algorithm Research Articles

Exploring a Student-Centered One-Stop Community Service Model

Abstract The student community in colleges and universities is based on students’ common living areas, and the community service model in colleges and universities should be student-oriented and centered on students’ development. This paper proposes a one-stop community service model from a student-oriented perspective, with the service community model and service recommendation model being the main component modules. In the service community model, a context-based association relationship mining algorithm is proposed to add time and location contexts to the collaborative filtering algorithm in order to obtain a collection of similar users and services. After constructing the one-stop service community, a service recommendation algorithm based on a trusted coalition is proposed to introduce student credibility and service usage frequency to achieve personalized recommendations of services. University H’s student community implemented the one-stop community service model. After the practice, the mean value of each dimension of the community’s service mode and content evaluation was greater than 3, and the overall satisfaction evaluation value of the community was 39.49, which was extremely significant compared with the evaluation value of University C (P<0.01). The mean value of students’ mental health evaluation reached 3.33.

A Service Recommendation Algorithm Based on Self-Attention Mechanism and DeepFM

This article proposes a recommendation model based on self-attention mechanism and DeepFM service, the model is SelfA-DeepFM. The method firstly constructs the service network with DTc-LDA model to mine the potential relationship between Mashup and API, which not only fully considers the text attributes but also combines the network structure information to effectively mitigate the sparsity of the service data. Secondly, service clustering to obtain numerical feature similarities. Finally, the self-attention mechanism is used to capture the different importance of feature interactions, and the DeepFM model is used to mine the complex interaction information between multidimensional features to predict and rank the quality score of API services to recommend suitable APIs. To verify the performance of the model, the authors use the real data crawled from the ProgrammableWeb platform to conduct multiple groups of experiments. The experimental results show that the model significantly improves the accuracy of service recommendation.

QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results.

Deep Neural Network Security Collaborative Filtering Scheme for Service Recommendation in Intelligent Cyber–Physical Systems

Cyber–physical systems (CPSs) is a security real-time embedded system. CPS integrates the information sensed by the current physical sensors, through high-speed real-time transmission, and then carries out powerful information processing to effectively interact and integrate the physical and the information worlds. With the aim to improve the quality of service, optimize the existing physical space, and increase security, collaborative filtering algorithms have also been widely used in various recommendation models for Internet of Things (IoT) services. However, general collaborative filtering algorithms cannot capture complex interactive information in the sparse Mashup–Web service call matrix, which leads to lower recommendation performance. Based on the artificial intelligence technology, this study proposes a recommendation algorithm for a security collaborative filtering service that integrates content similarity. A security collaborative filtering module is used to capture the complex interaction information between Mashup and Web services. By applying the content similarity module to extract the semantic similarity information between the Mashup and Web services, the two modules are seamlessly integrated into a deep neural network to accurately and quickly predict the rating information of Mashup for the Web services. Real data set on the intelligent CPS is captured and then compared with mainstream service recommendation algorithms. Experimental results show that the proposed algorithm not only efficiently completes the Web service recommendation task under the premise of sparse data but also shows better accuracy, effectivity, and privacy. Thus, the proposed method is highly suitable for the application of intelligence CPS.

An Efficient Service Recommendation Algorithm for Cyber-Physical-Social Systems

Cyber-physical-social System (CPSS) technology closely integrates and coordinates computing resources, physical resources, and social information to provide humans with efficient and convenient services. Service recommendation technology satisfies the individual needs of users by collecting, processing, and analyzing user characteristic data. In recent years, service recommendation algorithms based on social networks and collaborative filtering have been widely used in CPSS. However, this type of service recommendation algorithm cannot effectively use the aliasing and auxiliary information in the service, nor can it generate different service recommendation schemes according to different scenarios. We propose a collaborative filtering service recommendation algorithm that combines heterogeneous information networks and topic models for CPSS. The proposed algorithm designs a more effective functional similarity measurement method through the word vectors learned by Word2vec. Then, we use the topic model to cluster the scenario-based service (SBS), thereby greatly reducing the time required for service recommendation. Experimental analysis results show that we finally determine the best combination of Mashup similarity between the two scenarios with better recommendation efficiency and accuracy. Thus, the proposed recommendation algorithm can effectively improve the quality of service of CPSS, and provide a basis for the personalized service of CPSS.

Space-Based Information Service Recommendation Algorithm based on Hybrid Strategy

The recommendation technology has been applied to all walks of life, but it is rarely used in space-based information service. Considering the advantages of the recommendation technology and the particularity of space-based information service, a space-based information service recommendation algorithm based on hybrid strategy is proposed. Based on the different stages of the user, the similarity calculation is carried out from three aspects of user attributes, non-common service and service interest. The missing data in the service interest model is predicted by singular value decomposition to solve the problems of cold start and data sparse. Finally, the proposed algorithm is compared with the traditional algorithm, and the accuracy and scalability of the proposed algorithm are improved.

Improvement of Adaptive Learning Service Recommendation Algorithm Based on Big Data

In view of the problem that the traditional learning service recommendation does not fully consider the distinct differences between individuals, it is easy to lead to the contradiction between unchanging learning resources and learners’ personalized learning needs that are constantly improving, so an adaptive learning service recommendation improvement algorithm based on big data is proposed. Idea is based on adaptive learning platform and function modules. We consider the individual differences between students, to students as the center, collect students’ personalized learning demand data, and according to the data information to build student demand model. On the basis of using data mining methods for clustering recommendation service resources in learning, the adaptive recommend according to students’ individual need is proposed. The experimental results show that the adaptive learning service recommendation algorithm based on big data has high recommendation accuracy, coverage rate and recall rate, which is of great significance in the actual learning service recommendation.

Application of Travel Service Recommendation Algorithm Based on Cloud Computing

In the context of big data, traditional collaborative filtering recommendation algorithms cannot provide users with accurate recommendation services, making the sparsity of user data become an important factor affecting the accuracy of recommendation in the complex social network environment. This paper mainly studies the design and application of travel service recommendation algorithm based on cloud computing. In this paper, the data is processed based on MapReduce parallel computing framework to improve the performance and speed of the algorithm. The Linux-equipped cluster is deployed under the Hadoop framework. The cluster feasibility test is carried out to realize the recommendation function of the algorithm, and the function of the algorithm is realized according to the actual problems. On the Hadoop big data platform, the traditional algorithm was improved and the hadoop-based collaborative filtering recommendation algorithm was implemented to further improve the recommendation rate and accuracy of the traditional recommendation filtering algorithm.

A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

With the rapid development of the Internet, the number of Web APIs is increasing. How to recommend accurate and appropriate Web APIs to mashups has become a focus and difficulty in the field of service computing. The existing methods are mainly based on collaborative filtering technology, but these methods have problems such as the data sparsity and cold start, which leads to poor recommendation effects. This paper proposes a service recommendation model based on knowledge graph and collaborative filtering. In this model, the knowledge graph connects the APIs and mashups related information to mine the potential relations between mashups and APIs, hence reducing the impact of data sparsity. All the API entities in the service knowledge graph are embedded into the low-dimensional space through the representation learning algorithm, then the distances between the API vectors are calculated to recommend the related APIs. In addition, in order to solve the cold-start problem of recommending APIs to target mashups that have no APIs usage, the similarities of functional sets extracted from mashups are calculated to recommend APIs for target mashups. At the same time, the model obtains the Mashup-API usage record, using the technology of collaborative filtering to recommend appropriate APIs to target mashups. Finally, the similarities of the above recommended APIs are normalized and sorted to form the final recommendation result. The experimental results show that our proposed model significantly improves the accuracy of service recommendation.

A novel Customer Service Recommendation Algorithm for Power Users

Aiming at the large amount of power user data and the fact that the collaborative filtering recommendation technology fails to consider the relationship between users and customer service staff, a k-means clustering and user portrait recommendation method is proposed. This method firstly uses clustering technology clustering the power users’ portrait label vectors to gather similar users together, and makes recommendations based on the cluster to which the users belong. Secondly, through calculating the user portrait label vector similarity between the attributes of the service feature vector to establish the connection between the user and the customer service, and improve the traditional grading forecast method, the user and the personnel of the service of similarity index is integrated into it. Finally, the personnel of the service will be recommended from the two aspects of service quality and service fits.

A Novel Trust-Based Privacy Preservation Framework for Service Handling via Ontology Service Ranking

Pervasive computing has been a buzzing phrase these days as many areas accelerate their pace in developing pervasive computing systems. A pervasive computing aims at incorporating the computing technologies in a graceful and transparent manner, and make computing solutions available anywhere and at any time. Different aspects of pervasive computing, like smart homes, smart offices, social networks, micromarketing applications, PDAs, etc. are becoming a part of everyday life. However, ensuring privacy in pervasive computing has been a significant challenge that needs to be addressed for efficient adaptation of pervasive computing. In education domain, providing authentication for users poses difficulties in determining trustworthiness. In order to overcome the aforementioned challenges, a novel trust-based privacy preservation framework via ontology service ranking (TBPF-OSR) for user authentication is proposed in this work. At first, registration process is performed where a designated role manager assigns specific roles for all users based on their accessibility. Also, a new user generation methodology is allotted via generating user registration form. Further, authentication process is performed where the user request are validated based on the assigned roles. Finally, service handling is performed to effectively handle the requests generated in order to ensure service robustness and access control. Additionally, ontology based service ranking is performed via fuzzy logic based service recommendation algorithm.

A microservice recommendation mechanism based on mobile architecture

The microservice recommendation mechanism, as a key supporting technology for providing users with intelligent and proactive application services, is one of the important research contents in mobile social networks. In this paper, a recommendation mechanism of mobile social network service based on user trajectory, trust network and collaborative filtering algorithm is proposed. The user trajectory prediction model based on Generative Adversarial Network (GAN) is introduced into the process of service recommendation, and combined with trust degree to constitute the 3D service recommendation model of user-service-trajectory prediction. This method improves the accuracy and reliability of service recommendation and avoids the blindness and randomness of service recommendation. The simulation experiments show that the service recommendation algorithm has higher accuracy than the traditional collaborative filtering algorithm.

A service recommendation algorithm with the transfer learning based matrix factorization to improve cloud security

Recommendation system (RS) is designed to provide personalized services based on the users’ historical data. It has been applied in various fields and is expected to recommend the suitable services for the different kinds of users. Considering the importance of individual privacy, current users gradually tend not to expose personal information. This means RS may face the highly sparse datasets in the fields of cloud security. In general, the accuracy of recommendation will be improved with the growth of individual data, but the cold start problem is exactly in this contradictory phenomenon: this question evolves to produce sufficiently accurate recommendation result under the data scarcity problem. RS has to recommend services for the rarely historical data users and the latent users might drain along with the production of counter effects. To alleviate data scarcity problem in cloud security environment, this work is to introduce similar domain knowledge based on the transfer learning. Besides, the content and location based methods have been proved that these ideas work under this situation. So, this work also employs latent dirichlet allocation (LDA) to analysis the service descriptions and explore the relationship between the content and location information. In this framework, the suitable combination of LDA and word2vec models will balance the accuracy and speed which benefit service recommendation particularly. The related experiments demonstrate the effectiveness on the real word dataset. It can be found that the transfer learning based word2vec model shows the potentiality to explore the relationship between topic words, and improve the LDA algorithm from the content relationship. This proves that in both cold start environment and warm start environment, the proposed algorithm is more robust than other model-based state-of-art methods.

A Novel Approach for Web Service Recommendation Based on Advanced Trust Relationships

Service recommendation is one of the important means of service selection. Aiming at the problems of ignoring the influence of typical data sources such as service information and interaction logs on the similarity calculation of user preferences and insufficient consideration of dynamic trust relationship in traditional trust-based Web service recommendation methods, a novel approach for Web service recommendation based on advanced trust relationships is presented. After considering the influence of indirect trust paths, the improved calculation about indirect trust degree is proposed. By quantifying the popularity of service, the method of calculating user preference similarity is investigated. Furthermore, the dynamic adjustment mechanism of trust is designed by differentiating the effect of each service recommendation. Integrating these efforts, a service recommendation mechanism is introduced, in which a new service recommendation algorithm is described. Experimental results show that, compared with existing methods, the proposed approach not only has higher accuracy of service recommendation, but also can resist attacks from malicious users more effectively.

An Efficient Knowledge-Graph-Based Web Service Recommendation Algorithm

Using semantic information can help to accurately find suitable services from a variety of available (different semantics) services, and the semantic information of Web services can be described in detail in a Web service knowledge graph. In this paper, a Web service recommendation algorithm based on knowledge graph representation learning (kg-WSR) is proposed. The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. By calculating the distance between service entities in low-dimensional space, the relationship information of services which is not considered in recommendation approaches using a collaborative filtering algorithm is incorporated into the recommendation algorithm to enhance the accurateness of the result. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate, and coverage rate of recommendation but also solve the cold start problem to some extent.

Service recommendation based on description reconstruction in cloud manufacturing

ABSTRACT Cloud manufacturing has become an important development trend in the manufacturing industry. Manufacturing companies package their resources and capabilities as manufacturing services and publish on cloud manufacturing service system. With the rapid increase of a number of services being published on the service system, information overload becomes an issue. To identify functional requirements accurately and create service compositions in a timely manner, effective manufacturing service recommendation algorithm is urgently needed. Most traditional recommendation methods ignore the evolving characteristics of the cloud manufacturing service system and rely on initial static service descriptions. These descriptions are usually neither comprehensive nor pertinent in describing service application scenarios. To solve such issues, a novel method of Time-aware Targeted Reconstructing Service Descriptions (T-TRSD) is proposed in this paper. T-TRSD aims to reconstruct service descriptions by adding service composition descriptions. The evolving characteristics of services are also taken into consideration by the algorithm. This model complements the potential application scenarios of services, identifies the application scenario of the specific requirement and gives this scenario a higher weight. Based on service descriptions reconstructed by T-TRSD, a new manufacturing service recommendation strategy is offered. Comprehensive experiments show that this method brings a better recommendation performance.

Personalised service recommendation process based on service clustering

Personalised service recommendation is the key technology for service platforms; the demand preferences of users are the important factors for personalised recommendation. First, in order to improve accuracy and adaptability of service recommendation, services are needed to be initialised before being recommended and selected, then they are classified and clustered according to demand preferences, and service clusters are defined and demonstrated. In the sparse problem of service function matrix, historical and potential preferences are expressed as double matrices. Second, service cluster is viewed as the basic business unit, we optimise graph summarisation algorithm and construct service recommendation algorithm SCRP, helped by the experiments about variety parameters, which has more advantages than other algorithms. Third, we select fuzzy degree and difference to be the two key indicators, and use some service clusters to complete simulating and analyse algorithm performances. The results show that our service selection and recommendation method is better than others, which might effectively improve the quality of service recommendation.

An Effective and Trustable Spatial Service Recommendation Algorithm for Spatial Query Retrieval In Geo-Social Network

<span>The spatial information (e.g., restaurants/hotels) is related with the keyword(s) to indicate their businesses, services and features. The main issue of relevant information retrieval is to query an entity which includes a set of spatial query keywords and have the smallest amount of inter-object distance. The spatial queries with keywords have not been extensively explored. Still, the traditional method was focused on the multidimensional data. Previous works mostly targeted to predict the top-k Nearest Neighbors keyword query, where every keyword should be equivalent to the whole querying keywords. However, the mechanism does not consider the density of data entities in the spatial space. To overcome the above issues, An Effective and Trustable Spatial Service Recommendation (ETSSR) algorithm focuses on the most relevant information retrieval with the enhanced accuracy and minimal retrieval time for spatial information services. The main goal of work is to provide best spatial information retrieval with an accurateness of location prediction and minimal information retrieval time. The system minimizes the classification issue and visualization problem for spatial information </span><span>in Geospatial Social network. The system improves the spatial information retrieval with an accuracy of location prediction and minimizes the information retrieval time compare than existing methods. Based on Experimental estimations, proposed ETSSR+KNN enhanced 0.48 P (Precision) and 0.49 R (Recall) and minimized 28 milliseconds query retrieval time.</span>

Web Service Recommendation With Reconstructed Profile From Mashup Descriptions

Web services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component services from a sea of service candidates is often challenging. Therefore, web service recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by service providers. In this paper, we propose a novel way of dynamically reconstructing objective service profiles based on mashup descriptions, which carry historical information of how services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of services and bridge the vocabulary gap between mashup developers and service providers. Specifically, we jointly model mashup descriptions and component service using author topic model in order to reconstruct service profiles. Exploiting word features derived from the reconstructed service profiles, a new service recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed service recommendation algorithm is effective and outperforms the state-of-the-art methods. Note to Practitioners —Service recommendation accuracy for mashup creation is often limited due to poor quality of service descriptions. Mashup descriptions contain valuable information about functions and features of its component services, which can be leveraged to enhance descriptive quality of original service profiles. Based on the assumption, this paper proposes a novel two-phase service recommendation framework to facilitate mashup creation. Specifically, our approach reconstructs service profiles by extracting appropriate words from historical mashup descriptions. Then, a novel service recommendation algorithm is developed by exploiting popularity and relevance measures hidden in the reconstructed profiles. Moreover, we propose the rules of dominant words discovery and employ it to further refine our algorithm.

A Service Recommendation Algorithm Based on Modeling of Dynamic and Diverse Demands

This article contends that current service recommendation algorithms are still unable to meet the dynamic and diverse demands of users, so a service recommendation algorithm considering dynamic and diverse demands is proposed. The latent Dirichlet allocation model of machine learning field is adopted to extract the user implicit demand factors, and then the bipartite graph modeling and random-walk algorithm are used to extend implicit demand factors to predict short-term changes and diversity of user demand. At last, the service recommendation list is generated based on these demand factors. Experimental results on a real-world data set regarding service composition show that the proposed algorithm can represent dynamic and diverse user demands, and the performance of the proposed algorithm is better than that of the other algorithms in terms of accuracy, novelty, and diversity.