Service Recommendation System Research Articles

Multi-representation web service recommendation system based on attention mechanism

Multi-representation web service recommendation system based on attention mechanism

A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.

Service Recommendation Method based on Multi Model Fusion

<p>In recent years, the rapid development of service-oriented computing technology has increased the burden of choice for software developers when developing service-based applications. Existing Web service recommendation systems often face two challenges. First, developers are required to input keywords for service search, but due to their lack of knowledge in the relevant field, the keywords entered by the developers are usually freestyle, causing an inability to accurately locate services. Second, it is exceedingly difficult to extract services that meet the requirements due to the 99.8% sparseness of the application service interaction records. To address the above challenges, a framework for service recommendation through multi-model fusion (SRM) is proposed in this paper. Firstly, we employ graph neural network algorithms to deeply mine historical records, extract the features of applications and services, and calculate their preferences. Secondly, we use the BERT model to analyze text information and use the attention mechanism and fully connected neural networks to deeply mine the matching degree between candidate services and development requirements. The two models mentioned above are further merged to obtain the final service recommendation list. Extensive experiments on datasets demonstrate that SRM can significantly enhance the effectiveness of recommendations in service recommendation scenarios.</p> <p> </p>

Recommendation System for Mobile Applications Tour Guide and Travel Services using Demographic Filtering and Content-Based Filtering Methods based on Android

This research aims to build a tourism service recommendation system by providing recommendations for tour guides and tourism destinations using machine learning recommendation system methods. The recommendation system method in this research uses the demographic filtering method to recommend tour guides using a model with three stages, namely filtering, scoring and sorting the tour guide data category. At the scoring stage, it is carried out using a decision-making system method using the Simple Additive Weighting method to weight the tour guide value with the weighting value of each criterion in the tour guide data. This research also applies a content-based filtering method using a model with two stages, namely training and recommendation. The training stage uses tf-idf weighting and the recommendation stage uses cosine similarity to recommend tourism spots based on the similarity of metadata for each item. In using the recommendation system, the data will focus on collecting tour guide data and data on tourist attractions selected by the user. This data will be processed by a recommendation system using demographic filtering and content-based filtering methods. Through the Simple Additive Weighting method in demographic filtering and tf-idf weighting with consistent similarity in content-based filtering, this research found personalized recommendation results so that the level of accuracy of the recommendation results becomes more personal and accurate. The results of this research provide a recommendation system for tourist guides and tourist attractions which is implemented into an Android-based mobile application that can be used to meet tourists' needs.

Retracted: Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

Retracted: Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

Aspect term extraction via adaptive fusion of sequential and hierarchical representation

In aspect-based sentiment analysis, a fundamental task is extracting aspect terms from opinionated sentences. Aspect term extraction (ATE) has been found to play a critical role among several scenarios, such as service quality improvement and recommendation systems. While deep learning-based methods have achieved great progress in ATE, they mainly consider sequential semantic information and generally ignore the utilisation of syntactic relations of the whole sentence on overall meanings. Furthermore, performances of these methods may also be diminished by poor handling of relation and text noises. To address these issues, we propose a fused sequential and hierarchical representation (FSHR) model, wherein both sequential and hierarchical representations are generated, which facilitates not only the capture of linear semantic information for predicting meaning-related aspect terms but also the utilisation of syntactic relations over the entire sentence to better identify structure-related aspect terms. Moreover, to refine the aspect representation, we incorporate relation-gate mechanism which selectively activates meaningful syntactic dependency paths and design the multi-way aspect attention which prompts the model to focus on relevant text segments about particular aspects. Eventually, sequential and hierarchical representations are adaptively fused for aspect prediction. Experiment results on four datasets demonstrate that FSHR outperforms competitive baselines, and further extensive analyses reveal the effectiveness of our model.

A novel light GBM-optimized long short-term memory for enhancing quality and security in web service recommendation system

A novel light GBM-optimized long short-term memory for enhancing quality and security in web service recommendation system

Keyword-Driven Service Recommendation Via Deep Reinforced Steiner Tree Search

Developers need to reuse web services and create mashups suitable for various scenarios. Currently, it relies on the developer’s adequate domain knowledge to be able to find services and verify their compatibility. Although service recommendation systems already exist to assist them, inexperienced developers may not be able to adequately express their requirements, resulting in inappropriate and incompatible recommendations. To tackle this problem, we define a service-keyword correlation graph (SKCG) to capture the relationship between services and keywords, and the compatibility among services. Then, we propose keyword-based deep reinforced Steiner tree search (K-DRSTS) to recommend services for mashup creation. K-DRSTS models the task of service discovery as a Steiner tree search problem against SKCG. Leveraging deep reinforcement learning, K-DRSTS provides an efficient solution for solving the NP-hard search problem of the Steiner tree. Extensive experiments on real-world data sets have shown the effectiveness of K-DRSTS.

Study On Social Network Recommendation Service Method Based On Mobile Cloud Computing

In order to improve the recommendation ability of social network, the design method of social network recommendation service system based on mobile cloud computing is proposed. Design the topology of social network recommendation service and retrieve the service information of social network. The correlation characteristics of intelligent social network recommendation are extracted, collaborative filtering is carried out, the filtering detection model of social network service is established, and the filtering analysis in the process of social network service recommendation is realised by using autocorrelation feature matching. In the intelligent service mode, the trust relationship scheduling of social network resources is realised, and according to the trust relationship, the system optimisation design of social network recommendation service is realised in the mobile cloud computing environment. The experiment shows that the error of this method is relatively small, the lowest can reach 0.62, and it has good intelligence, accuracy and anti-interference ability.

Research on the precise recommendation service system of digital library

With the continuous development of big data technology, the service mode of library has changed greatly. This paper proposes a method for building a digital library precise recommendation service system based on user portraits, which improves the recommendation accuracy and efficiency of the digital library. Firstly, systematically sort out the research status of the application of user portrait technology in the field of library and information. Then, the needs of precise recommendation of the digital library under the user portrait technology in terms of recommended content, service positioning, user functions were analyzed. Finally, introduce the construction method of the recommendation service system of the digital library from the three aspects of data module, recommendation module and user module. This research aims to explore an effective transformation and development path for the precise recommendation of digital library.

Optimization of Digital Recommendation Service System for Tourist Attractions Based on Personalized Recommendation Algorithm

With the deepening of tourists’ demand for tourism services, the personalization of online tourists has gradually become an application of personalized recommendation technology. According to the application requirements of personalized scenic spot recommendation, this paper uses social networks and Bayesian networks to fully mine the matching degree between users and scenic spots for personalized recommendation. Add social network factors to the recommendation of tourist attractions, and fully tap the social network relationship between users. First, the users are clustered by the coupling bidirectional clustering algorithm. Then, DBSCAN (density-based noise application spatial clustering) algorithm is used to cluster scenic spots. Finally, two stable user sets and scenic spot sets are applied to the personalized recommendation algorithm to predict the user’s next upcoming scenic spot. The algorithm is compared with some traditional algorithms in the dataset. The algorithm deals with the similarity of user attributes and user behavior and uses content-based algorithm to deal with the relationship between scenic spots; com datasets have better performance.

Retraction Note to: Cloud service recommendation system based on clustering trust measures in multi-cloud environment

Retraction Note to: Cloud service recommendation system based on clustering trust measures in multi-cloud environment

Effects of Feature-Based Explanation and Its Output Modality on User Satisfaction With Service Recommender Systems.

Recent advances in natural language based virtual assistants have attracted more researches on application of recommender systems (RS) into the service product domain (e.g., looking for a restaurant or a hotel), given that RS can assist users in more effectively obtaining information. However, though there is emerging study on how the presentation of recommendation (vocal vs. visual) would affect user experiences with RS, little attention has been paid to how the output modality of its explanation (i.e., explaining why a particular item is recommended) interacts with the explanation content to influence user satisfaction. In this work, we particularly consider feature-based explanation, a popular type of explanation that aims to reveal how relevant a recommendation is to the user in terms of its features (e.g., a restaurant's food quality, service, distance, or price), for which we have concretely examined three content design factors as summarized from the literature survey: feature type, contextual relevance, and number of features. Results of our user studies show that, for explanation presented in different modalities (text and voice), the effects of those design factors on user satisfaction with RS are different. Specifically, for text explanations, the number of features and contextual relevance influenced users' satisfaction with the recommender system, but the feature type did not; while for voice explanations, we found no factors influenced user satisfaction. We finally discuss the practical implications of those findings and possible directions for future research.

Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance Criteria

Businesses are overgrowing worldwide; people struggle for their businesses and startups in almost every field of life, whether industrial or academic. The businesses or services have multiple income streams with which they generate revenue. Most companies use different marketing and advertisement strategies to engage their customers and spread their services worldwide. Service recommendation systems are gaining popularity to recommend the best services and products to customers. In recent years, the development of service-oriented computing has had a significant impact on the growth of businesses. Knowledge graphs are commonly used data structures to describe the relations among data entities in recommendation systems. Domain-oriented user and service interaction knowledge graph (DUSKG) is a framework for keyword extraction in recommendation systems. This paper proposes a novel method of chunking-based keyword extractions for hybrid recommendations to extract domain-specific keywords in DUSKG. We further show that the performance of the hybrid approach is better than other techniques. The proposed chunking method for keyword extraction outperforms the existing value feature entity extraction (VF2E) by extracting fewer keywords.

A service recommendation system based on rough multidimensional matrix in cloud-based environment

In order to efficiently and accurately realize the service recommend to cloud-based environment, a collaborative filtering algorithm using rough decision mechanism in handling the uncertainty is established. We proposed a service recommendation system in cloud-based environment that helps a user to select the best services from different cloud providers that match user requirements. The new system combines items' attributes information (decision tags) and user's attribute information (decision times), achieves high performance and alleviates the cold-start problems of recommendation systems. A rough multidimensional matrix model based on cooperative filtering algorithm is proposed. Specifically, we design useful selection criteria based on items' attributes and users' attributes, and combine the criteria in an optimization framework of cloud-based environment. Experiments show that this method can effectively solve the cold start problem of new projects, and is more accurate than the traditional collaborative filtering algorithm.

Hybrid collaborative filtering model for consumer dynamic service recommendation based on mobile cloud information system

The rapid development of the web has led to a considerable increase in information dissemination. Recently, personalized web service recommendation has become a popular research area in service computing. Research on web service recommendation systems mainly addresses two problems: prediction and completion of sparse QoS data, and the user's personalized recommendation. To address the issue of high data sparsity and low recommendation accuracy in the traditional service recommendation models under mobile cloud, this study presents a hybrid collaborative filtering model for consumer service recommendation based on mobile cloud by introducing user preferences. The example verified that the service recommendation based on the model can effectively reduce the data sparsity and increase the accuracy of the prediction.

To Nudge or Not to Nudge: News Recommendation as a Tool to Achieve Online Media Pluralism

Recent policy documents propose the use of recommender technology to realise online media pluralism goals. Specifically, they may enable citizens to access a diversity of viewpoints concerning matters of public interest via digital means (“public service recommender systems”). On the basis of Article 10 ECHR, States are arguably required to guarantee such “online access diversity.” At the same time, they must, pursuant to that same provision, at all times ensure that individuals maintain autonomy and freedom of choice as regards whether or not to consume certain content. In that context, this contribution argues that policymakers may consider “nudging” people toward reading diversely through recommendation (libertarian-paternalistic approach). Additionally, it points to the possibility of merely offering users the tools to do so, thereby allowing them to make an (informed) choice as regards whether to use them or not (libertarian approach). The first approach presumably best mitigates chances of selective exposure, whilst the second is more “liberty-preserving.” In either case, States comply with their positive and negative obligations stemming from the freedom to receive information and ideas (without interference by public authority) and the freedom to hold opinions.

IQSrec: An Efficient and Diversified Skyline Services Recommendation on Incomplete QoS

Recent developments of Internet technologies have accelerated the growth of Web services (<i>e.g.</i>, open APIs). As many services provide similar functionality, service recommendation systems use the Quality of Service (QoS) to help users find optimal services. Space partition attracts significant attention in service recommendation since it improves the diversity of recommendations and accelerates skyline services query. However, existing partition-based service recommendation systems are all implemented on complete QoS. They are not sufficient when some services&#x2019; QoS values are missing or invalid. To this end, we develop a new partition-based service recommendation method on incomplete QoS (named IQSrec) that combines probabilistic skyline query and space partition. The probabilistic skyline query measures top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> skyline services on incomplete QoS. A dimension-based partition is specially designed for splitting the incomplete QoS service space into <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula>-dimensional partitions with the most representative services. The candidate skyline services are chosen from each partition and merged together for probabilistic skyline computation. IQSrec selects the highest skyline probability services in each partition as recommendations. The experiments on the synthetic and real-world datasets show IQSrec can efficiently recommend skyline services on incomplete QoS. IQSrec has higher accuracy and diversity compared to the state-of-the-art service recommendation approaches.

Automatic car service recommendation system using machine learning techniques

The automobile industry has been growing at a high rate in the past few decades, contributing about 7.5% to India's total Gross Domestic Product (GDP). As the number of vehicle owners are increasing the demand and need for automobile service is also high, but people are busy with their routines, hence failing to perform proper maintenance on their vehicles. This paper uses machine learning algorithms and object detection to come up with the idea to develop a web application that suggests users some offers and timing for their car maintenance by analyzing a car using computer vision without the owner's involvement. This project aims at both the owner's convenience and the growth of the service provider's business. Generally, we do not realize that multiple tasks can be done at a time, which results in incomplete tasks. This paper presents a machine learning-based automated car maintenance system with effective time utilization, by using the Internet of Things (IoT) device that could be installed at the parking's main gate in places where people tend to spend many hours, like offices or malls. This device consists of a camera that is responsible for detecting a car image from the live video. These images are then sent to the device, which uses pre-trained models to detect any damages or dirtiness in the vehicle.

Ontology Rules for Library Research Services Recommendation System

This research aims to present the design and construction of guidelines on the ontology rules for research support services of the Khon Kaen University Library. In this research, there was a usage of Protégé version 3.5 to build an ontology into a knowledge base in the form of Ontology Web Language (OWL). The results showed ten ontology rules: 1) Rules for plagiarism recommendations 2) Rules for plagiarism recommendations on journal quality audits for academic publications 3) Rules for displaying recommendations on information retrieval in Web OPAC 4) Rules for displaying recommendations for information literacy training programs 5) Rules for displaying recommendations on thesis database lists 6) Rules for displaying recommendations 7) Rules for displaying instructions on e-journals databases, 8) Rules for displaying instructions on citations in the next section, 9) Rules for displaying instructions on endnote citations, and 10) Rules for displaying advice on research aids. The measurement accuracy (Precision) was 92.73%, the completeness (Recall) was 90.74%, and the F-measure was 91.72%.