Collaborative Recommendation Approach Research Articles

Similarity-based Graph Convolution Collaborative Recommendation Approach

With the rapid and iterative development of science and technology, a large amount of information exceeds the range that can be accepted, processed, or effectively utilized by an individual or a system, and recommendation algorithms can, to a certain extent, solve such problems, but traditional recommendation algorithms do not have a good solution to the problems related to data sparsity and recommendation accuracy. A similarity-based graph convolutional neural collaborative recommendation method (GCSCF) is proposed. The similarity algorithm based on the attributes of the item is used to find the item with the highest similarity and has not interacted with the current user, and this item is set to interact with the current user. The relevant interaction information of the user and the item is converted into relative feature vectors; the feature vectors are propagated using a graph convolutional neural network to aggregate the localized information, and the weight coefficients based on the item ratings are normalized to reduce the noise caused by the information aggregation. Comparative experiments are conducted on two public datasets, MovieLens-1M and Movielens-100K, with five baseline models on the set, and using Recall, Normalized Discounted Cumulative Gain (NDCG), and Precision as the evaluation metrics, and the results of the experiments show that the performance of the proposed social recommendation model better than other models.

Group-aware graph neural networks for sequential recommendation

Sequential recommendations play a crucial role in recommender systems. Existing methods commonly focus on extracting sequential patterns within individual item sequences to make personalized recommendations. However, this paper argues that such a closure strategy has limitations. Firstly, the inherent uncertainty and sparsity within these sequences pose considerable challenges in accurately capturing sequential patterns. Secondly, ignoring group behaviors beyond the sequence hinders the modeling of user similarities, which are essential for recommendations. To address these limitations, this paper proposes a novel collaborative sequential recommendation approach, called Group-aware Graph Neural Networks (GaGNN). GaGNN seeks to incorporate group behaviors within sequential recommendations, thereby enhancing the modeling of user preferences through the assimilation of collective wisdom. GaGNN constructs two transition graphs, each offering a distinct behavioral perspective. The group transition graph is designed to capture the collaborative effects and learn collaborative information, whereas the individual transition graph aims to articulate individual behaviors, thereby extracting sequential information. Additionally, a multi-source information fusion module is designed to bridge semantic gaps between the two types of information and yield a holistic understanding of user preferences. Through rigorous experimental comparisons and ablation studies, the superiority of GaGNN and the beneficial integration of collaborative information are emphatically validated.

A Recommendation Method for an Online Programming Portal

An online programming portal is a valuable resource for Information Technology students to enhance their programming skills. It offers an environment where educators and learners can create problems, generate test data, program, and automatically evaluate tests. Numerous universities, both domestic and international, have achieved remarkable success by developing digital content for these portals. However, a common challenge for learners is locating problems that align with their expertise in the vast database covering various topics. In this paper, we propose a collaborative filtering recommendation approach integrated into the Online Programming Portal. This method aims to suggest a suitable set of problems based on each user’s programming proficiency. Experiments conducted on the Online Programming Portal at the Post & Telecommunications Institute of Technology (PTIT) demonstrate a significant enhancement in students’ online problem-solving results.

Scalable and explainable visually-aware recommender systems

Recommender systems are popularly used to deal with an information overload issue. Existing systems mainly focus on user–item interactions and semantic information derived from metadata of users and items to improve recommendation accuracy. Item images provide useful information to infer users’ individual preferences, especially for those domains where visual factors are influential such as fashion items. However, this type of information has been ignored by most previous work. To bridge this gap and meet the requirements of performance from the aspects of Accuracy, Scalability, and Explainability evaluation metrics, this paper proposes a scalable and explainable visually-aware recommender system framework called SEV-RS. This framework contains a visually-augmented heterogeneous information network, a scalable meta-path feature extraction method for multi-hop relations, and a shallow explainable meta-path based Collaborative Filtering recommendation approach. We compared SEV-RS with the state-of-the-art models such as the deep learning model using Graph Attention Network on two real-world datasets and one synthetic dataset. The results show that SEV-RS produced more accurate and more explainable recommendations. Also, SEV-RS has substantially less computational time than the compared deep learning models.

Topic optimization–incorporated collaborative recommendation for social tagging

PurposeWith the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.Design/methodology/approachCombining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.FindingsExperimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.Originality/valueWith the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

Robust Collaborative Filtering Recommendation With User-Item-Trust Records

The ever-increasing popularity of recommendation systems allows users to find appropriate services without excessive effort. However, due to the unstable and complex network environment, the historical behavior data of users are quite sparse in most cases. The inherent drawbacks render preference prediction infeasible for cold-start users and have become a crucial issue to be resolved in recommendation systems. To deal with the problems, we first present a Trust-based Collaborative Filtering (TbCF) algorithm to perform basic rating prediction in a manner consistent with the existing CF methods. Then, we propose the Hybrid Collaborative Filtering Recommendation approach with User-Item-Trust Records ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {UIT}_{\text {hybrid}}$ </tex-math></inline-formula> ), a novel approach that incorporates user trust into the existing CF-based methods in a harmonious way to supplement rating information. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {UIT}_{\text {hybrid}}$ </tex-math></inline-formula> employs multiple perspectives to extract proper services and achieves a good tradeoff between the robustness, accuracy, and diversity of the recommendation. We conduct extensive real-world experiments on the Epinions data set to demonstrate the feasibility and efficiency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {UIT}_{\text {hybrid}}$ </tex-math></inline-formula> .

New Strategy Based on RBF Network to Develop a Collaborative Filtering Recommender System

Collaborative filtering is a popular recommendation algorithm. It predicts user's interests according to the ratings or behaviour of other users in the system. However, the collaborative filtering recommender system suffers from several major limitations including scalability, sparsity, and cold start. In this paper, a collaborative filtering recommendation approach using radial basis function (RBF) network and power method is proposed. The proposed system has offline and online phases. In the offline phase, the sparse user-item rating matrix is completed by using RBF network based on Cover's theorem on the separability of patterns. RBF network learning is done by unsupervised kernel-based fuzzy c-means clustering algorithm for selecting RBF centers, and supervised gradient descend method for selecting RBF weights. In the offline phase, we predict non-rated items of a user. Then the full rating matrix is used to rank all the users. The ranking is done by solving an eigenvalue problem. This paper overcomes the scalability problem by clustering the users, the sparsity problem by completing the sparse rating matrix, and the new user cold start problem by recommending the top rated items of the high-ranked user. The results of the experiments, on the benchmark data sets, show that the proposed system produces high quality recommendation, in terms of accuracy and quality.

Effective Hybrid Content-Based Collaborative Filtering Approach for Requirements Engineering

Requirements engineering (RE) is among the most valuable and critical processes in software development. The quality of this process significantly affects the success of a software project. An important step in RE is requirements elicitation, which involves collecting project-related requirements from different sources. Repositories of reusable requirements are typically important sources of an increasing number of reusable software requirements. However, the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform accurately. Recommender systems have been widely recognized as an effective solution to such problem. Accordingly, this study proposes an effective hybrid content-based collaborative filtering recommendation approach. The proposed approach will support project stakeholders in mitigating the risk of missing requirements during requirements elicitation by identifying related requirements from software requirement repositories. The experimental results on the RALIC dataset demonstrate that the proposed approach considerably outperforms baseline collaborative filtering-based recommendation methods in terms of prediction accuracy and coverage in addition to mitigating the data sparsity and cold-start item problems.

A novel Collaborative Filtering recommendation approach based on Soft Co-Clustering

Collaborative Filtering (CF) recommendation algorithm has been widely applied into recommender systems. Many CF algorithms associate a user/an item with one of subgroups by explicit or implicit features. However, considering that users may have multiple personalities and items may have diverse attributes, it is more reasonable to associate a user/an item with more than one group. In this paper, we propose the Soft K-indicators Alternative Projection (SKAP) algorithm, which can efficiently resolve soft clustering problem with high dimensions, to generate a sparse partition matrix and further a Top-N recommendation list is given. Unlike fuzzy C-means clustering, the SKAP algorithm is independent on the selection of initial values. In addition to that, we integrate the item type information into recommender systems to improve recommendation accuracy. Experimental results show that the proposed approach behaves superior performance in Top-N recommendation in terms of classical metrics and further show that multi-label classification framework is a better description than classical Co-Clustering framework.

Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism

The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

Context-Aware Recommendations Based on Deep Learning Frameworks

In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in various ways for multiple purposes, such as rating prediction, generating top-k recommendations, and classification of users’ feedback. Specifically, based on the suggested framework, we propose three deep context-aware recommendation models based on explicit, unstructured, and structured latent representations of contextual data derived from various contextual dimensions (e.g., time, location, user activity). Offline evaluation on three context-aware datasets confirms that our proposed deep context-aware models surpass state-of-the-art context-aware methods. We also show that utilizing structured latent contexts in the proposed deep recommendation framework achieves significantly better performance than the other context-aware models on all datasets.

A hybrid collaborative filtering recommendation algorithm for requirements elicitation

Requirements elicitation is one of the most critical and difficult tasks in software development. Requirements reuse has shown to be an effective and efficient elicitation technique that can enhance the quality of the requirements elicitation process and, as a result, lead to a project's success. However, the information overload problem, which is caused by the rapidly growing number of reusable software requirements in large requirements repositories, hinders the effectiveness of the requirements reuse process. Recommender systems proved to be a well-known solution to such problems. This paper focuses on the adoption of recommender systems to mitigate the problem of information overload that is inherent in the requirements elicitation process, specifically by assisting requirement engineers in retrieving relevant reusable requirements from large-scale requirements repositories. The validation results on the RALIC dataset illustrate that the proposed algorithm outperforms and mitigates the drawbacks of the benchmark collaborative filtering-based recommendation approaches.

A hybrid collaborative filtering recommendation algorithm for requirements elicitation

Requirements elicitation is one of the most critical and difficult tasks in software development. Requirements reuse has shown to be an effective and efficient elicitation technique that can enhance the quality of the requirements elicitation process and, as a result, lead to a project's success. However, the information overload problem, which is caused by the rapidly growing number of reusable software requirements in large requirements repositories, hinders the effectiveness of the requirements reuse process. Recommender systems proved to be a well-known solution to such problems. This paper focuses on the adoption of recommender systems to mitigate the problem of information overload that is inherent in the requirements elicitation process, specifically by assisting requirement engineers in retrieving relevant reusable requirements from large-scale requirements repositories. The validation results on the RALIC dataset illustrate that the proposed algorithm outperforms and mitigates the drawbacks of the benchmark collaborative filtering-based recommendation approaches.

A recommender system for selecting potential industrial training organizations

The difficulty in securing students industrial work experience scheme (SIWES) placements has negatively affected the final grades of some undergraduates. A recommender system is hereby proposed to solve this problem. This system will use past SIWES data to recommend potential organizations to future SIWES students. Research data collection was through an online questionnaire, with 200 respondents. The data was divided into a training data set (70%) and test data set (30%). Collaborative filtering recommendation approach was employed using the C4.5 algorithm to classify the data and generate a decision tree model from the training data set. The model generated was used to predict the class label of the test data set. Results from the data analysis carried out revealed that Kappa statistics was 0.7839, mean absolute error = 0.0058, and root mean squared error = 0.0586. In addition, true positive rate = 0.788, recall = 0.788, precision = 0.749, F‐measure = 0.755, MCC = 0.760, ROC = 0.985, and PRC = 0.813. The high values obtained indicates that the model was predicting with a high level of accuracy, ie, 78.84% correctly classified instances. The developed model was used as a knowledge base to develop a very beneficial front‐end web application, tagged “RecommendIT” where students can enter their preferences and view company recommendations.

A Regression-Based Collaborative Filtering Recommendation Approach to Time-Stepping Multi-Solver Co-Simulation

The ever-increasing application of modeling and simulation to the development of complex engineering systems has made co-simulation indispensable to the handling of coupled multi-domain models. The mechanism for controlling communication between multiple solvers holds the key to co-simulation performance and is regarded as one of the most challenging parts in co-simulation as a lot of tradeoffs need to be made in terms of stability, accuracy, and efficiency. As such, a holistic and dynamic approach is required, which has not been addressed by this paper that has a focus on either tailored problem with a specific numerical analysis scheme or software platforms for implementing data exchange. This paper precisely aims to address this gap by developing a knowledge-based approach to streamlining the co-simulation process. Specifically, a regression-based collaborative filtering approach is developed to recommend suitable ordinary differential equation solvers for individual simulators according to the specific engineering characteristics and historical simulation data. On this basis, the theoretical analysis of the stability region and truncation error is conducted to provide guidance on controlling time stepping of individual simulators using a Jacobi communication scheme. This approach has been evaluated in several computational experiments, in which the advantages of the proposed approach are demonstrated. First, the recommendation algorithm is reliable in making suggestions on viable solvers during simulation run time, especially when only sparse historical datasets are available. Second, the time-stepping scheme noticeably improves the computational efficacy owing to it having no dependence on the initial step-size choice, which is a more eminent advantage for high-fidelity co-simulation problems.

A knowledge-based multi-criteria collaborative filtering approach for discovering services in mobile cloud computing platforms

In the context of Cloud-based development of mobile applications, third-party services to be integrated by applications often have to be manually selected among many categories and providers at design time. Over the years, recommender systems have proven effective in overcoming the challenges related to the incredible growth of the information on the Web. In an effort to better address this problem, the use of Semantic Web technologies in the development of recommender systems has been gaining momentum in recent years. In this paper, we propose a knowledge-based Collaborative Filtering recommendation approach for the discovery of services in a mobile Cloud computing platform for services-based development. Our approach employs a knowledge-based technique that takes advantage of Semantic Web rule-based reasoning capabilities. A major contribution of this work is a multi-criteria collaborative service evaluation mechanism that is based on a standard service quality framework and is built on top of an ontology-based domain model. A two-part evaluation method that is intended to evaluate the proposed recommendation approach not only from a Computer Science perspective but also from an Information Systems perspective is also presented.

Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

In the era of big data, traditional computing systems and paradigms are not efficient and even difficult to use. For high performance big data processing, mobile edge computing is emerging as a complement framework of cloud computing. In this new computing architecture, services are provided within a close proximity of mobile users by servers at the edge of network. Traditional collaborative filtering recommendation approach only focuses on the similarity extracted from the rating data, which may lead to an inaccuracy expression of user preference. In this paper, we propose a cultural distance-aware service recommendation approach which focuses on not only the similarity but also the local characteristics and preference of users. Our approach employs the cultural distance to express the user preference and combines it with similarity to predict the user ratings and recommend the services with higher rating. In addition, considering the extreme sparsity of the rating data, missing rating prediction based on collaboration filtering is introduced in our approach. The experimental results based on real-world datasets show that our approach outperforms the traditional recommendation approaches in terms of the reliability of recommendation.

Integrated collaborative filtering recommendation in social cyber-physical systems

Cyber-physical systems are becoming part of our daily life, and a large number of data are generated at such an unprecedented rate that it becomes larger than ever before in social cyber-physical systems. As a consequence, it is highly desired to process these big data so that meaningful knowledge can be extracted from those vast and diverse data. Based on those large-scale data, using collaborative filtering recommendation methods to recommend some valuable clients or products for those e-commerce websites or users is considered as an effective way. In this work, we present an integrated collaborative filtering recommendation approach that combines item ratings, user ratings, and social trust for making better recommendations. In contrast to previous collaborative filtering recommendation works, integrated collaborative filtering recommendation approach takes full advantage of the correlation between data and takes into consideration the similarity between items, the similarity between users and two kind...

A PageRank-based collaborative filtering recommendation approach in digital libraries

A PageRank-based collaborative filtering recommendation approach in digital libraries

An explicit trust and distrust clustering based collaborative filtering recommendation approach

A SVD signs based trust and distrust clustering method is proposed.A trust inference method is proposed to compute indirect trust between users.A trust neighbors mining algorithm is proposed to discover trust users.A sparse rating complement algorithm is proposed to get dense user rating profiles.The TCCF method is efficient in terms of prediction accuracy and coverage. Clustering based recommender systems have been demonstrated to be efficient and scalable to large-scale datasets. However, due to the employment of dimensionality reduction techniques, clustering based recommendation approaches generally suffer from relatively low accuracy and coverage. To tackle these problems, some trust clustering based recommendation methods are proposed which cluster the social trust information other than the user-item ratings. Existing trust clustering based recommendation algorithms only consider trust relationships, regardless of the distrust information. In addition, these methods simply perform traditional collaborative filtering method in the detected trust communities, which cannot handle the data sparsity and cold start problems effectively. In order to solve these issues, in this paper, an explicit trust and distrust clustering based collaborative filtering recommendation method is proposed. Firstly, a SVD signs based clustering algorithm is proposed to process the trust and distrust relationship matrix in order to discover the trust communities. Secondly, a sparse rating complement algorithm is proposed to generate dense user rating profiles which alleviates the sparsity and cold start problems to a very large extent. Finally, the prediction of missing ratings can be obtained by combining the newly generated user rating profiles and the traditional collaborative filtering algorithm. Experimental results on real-world dataset demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation.