Recommendation Framework Research Articles

A framework for generating recommendations based on trust in an informal e-learning environment

Rapid advancement in information technology promotes the growth of new online learning communities in an e-learning environment that overloads information and data sharing. When a new learner asks a question, how a system recommends the answer is the problem of the learner’s cold start. In this article, our contributions are: (i) We proposed a Trust-aware Deep Neural Recommendation (TDNR) framework that addresses learner cold-start issues in informal e-learning by modeling complex nonlinear relationships. (ii) We utilized latent Dirichlet allocation for tag modeling, assigning tag categories to newly posted questions and ranking experts related to specific tags for active questioners based on hub and authority scores. (iii) We enhanced recommendation accuracy in the TDNR model by introducing a degree of trust between questioners and responders. (iv) We incorporated the questioner-responder relational graph, derived from structural preference information, into our proposed model. We evaluated the proposed model on the Stack Overflow dataset using mean absolute precision (MAP), root mean squared error (RMSE), and F-measure metrics. Our significant findings are that TDNR is a hybrid approach that provides more accurate recommendations compared to rating-based and social-trust-based approaches, the proposed model can facilitate the formation of informal e-learning communities, and experiments show that TDNR outperforms the competing methods by an improved margin. The model’s robustness, demonstrated by superior MAE, RMSE, and F-measure metrics, makes it a reliable solution for addressing information overload and user sparsity in Stack Overflow. By accurately modeling complex relationships and incorporating trust degrees, TDNR provides more relevant and personalized recommendations, even in cold-start scenarios. This enhances user experience by facilitating the formation of supportive learning communities and ensuring new learners receive accurate recommendations.

MF-GSLAE: A Multi-Factor User Representation Pre-training Framework for Dual-Target Cross-Domain Recommendation

MF-GSLAE: A Multi-Factor User Representation Pre-training Framework for Dual-Target Cross-Domain Recommendation

A multidimensional recommendation framework for identifying biological targets to aid the diagnosis and treatment of liver metastasis in patients with colorectal cancer

The quest to understand the molecular mechanisms of tumour metastasis and identify pivotal biomarkers for cancer therapy is increasing in importance. Single-omics analyses, constrained by their focus on a single biological layer, cannot fully elucidate the complexities of tumour molecular profiles and can thus overlook crucial molecular targets. In response to this limitation, we developed a multiobjective recommendation system (RJH-Metastasis 1.0) anchored in a multiomics knowledge graph to integrate genome, transcriptome, and proteome data and corroborative literature evidence and then conducted comprehensive analyses of colorectal cancer with liver metastasis (CRCLM). A total of 25 key genes significantly associated with CRCLM were recommended by our system, and GNB1, GATAD2A, GBP2, MACROD1, and EIF5B were further highlighted. Specifically, GNB1 presented fewer mutations but elevated RNA transcription and protein expression in CRCLM patients. The role of GNB1 in promoting the malignant behaviours of colon cancer cells was demonstrated via in vitro and in vivo studies. Aberrant expression of GNB1 could be regulated by METTL1-driven m7G modification. METTL1 knockdown decreased m7G modification in the 3’ UTR of GNB1, increasing its mRNA transcription and translation during liver metastasis. Furthermore, GNB1 induced the formation of an immunosuppressive microenvironment by promoting the CLEC2C-KLRB1 interaction between memory B cells and KLRB1+PD-1+CD8+ cells. GNB1 expression and the efficacy of PD-1 antibody-based treatment in CRCLM patients were significantly correlated. In summary, our recommendation system can be used for effective exploration of key molecules in colorectal cancer, among which GNB1 was identified as a critical CRCLM promoter and immunotherapy biomarker in colorectal cancer patients.

SABTR: semantic analysis-based tourism recommendation

Online tourism spot recommendations, as a key component of tourism services, aim to present travel options that align with users’ personal preferences. However, current recommendation systems often underperform due to the sparsity of tourism data and the wide variance in user preferences. To address this challenge, we propose a Semantic Analysis-Based Tourism Recommendation framework, abbreviated as SABTR (Semantic Analysis-Based Tourism Recommendation). The framework comprises two stages: Firstly, Latent Dirichlet Allocation (LDA) models are utilized to deeply mine data between users and attractions, constructing two core matrices: the user similarity matrix and the attraction similarity matrix. Secondly, based on the user similarity matrix, similarity calculation methods are applied to predict ratings for tourism spots that users have not yet evaluated. Simultaneously, within the attraction similarity matrix, probability distributions for each attraction across various thematic interests are calculated. When the system identifies a user’s interest in specific types of attractions, SABTR can select a series of related attractions from associated interest tags. Then, these candidate attractions are ranked according to both known and predicted user ratings, ultimately forming personalized attraction packages recommended to users. Extensive experiments have demonstrated that compared to existing tourism recommendation solutions, our method significantly improves the quality of attraction recommendations and enhances user satisfaction.

A Human Resource Evaluation and Recommendation System based on Big Data Mining

This investigation presents a paradigm-shifting Human Resource (HR) Assessment and Recommendation Framework, leveraging progressed huge information mining procedures. Drawing bits of knowledge from cybersecurity, healthcare, education, and further detecting spaces, our framework utilizes a different cluster of calculations, counting Arbitrary Forests, Support Vector Machines, K-Means Clustering, and a Feedforward Neural Organize. The comparative investigation uncovers the Feedforward Neural Network as the standout entertainer, emphasizing its various levels including learning for complicated design acknowledgement inside HR measurements. Uniquely, this framework draws on methodologies and insights from varied domains such as cybersecurity, healthcare, and education, applying these rich, interdisciplinary perspectives to HR analytics. This cross-pollination of ideas enables the framework to adopt sophisticated data mining and pattern recognition techniques that are not traditionally utilized within HR, offering new avenues for detecting and interpreting complex employee data patterns. Result values illustrate the system’s adequacy, with a precision of 88%, an accuracy of 90%, a review of 87%, and an F1 score of 88%. These measurements emphasize the system’s capacity to comprehensively assess worker execution, giving exact suggestions for key HR decision-making. Ethical contemplations, innovation acknowledgement, and custom-fitted proposal frameworks, propelled by related works, are coordinates to guarantee the system’s reasonability over assorted organizational settings. This research contributes to the advancing scene of HR administration, offering a spearheading arrangement for organizations looking for data-driven, comprehensive, and morally sound approaches to workforce optimization.

A novel framework for MOOC recommendation using sentiment analysis

A novel framework for MOOC recommendation using sentiment analysis

Multi-view multi-behavior interest learning network and contrastive learning for multi-behavior recommendation

The recommendation system aims to recommend items to users by capturing their personalized interests. Traditional recommendation systems typically focus on modeling target behaviors between users and items. However, in practical application scenarios, various types of behaviors (e.g., click, favorite, purchase, etc.) occur between users and items. Despite recent efforts in modeling various behavior types, multi-behavior recommendation still faces two significant challenges. The first challenge is how to comprehensively capture the complex relationships between various types of behaviors, including their interest differences and interest commonalities. The second challenge is how to solve the sparsity of target behaviors while ensuring the authenticity of information from various types of behaviors. To address these issues, a multi-behavior recommendation framework based on Multi-View Multi-Behavior Interest Learning Network and Contrastive Learning (MMNCL) is proposed. This framework includes a multi-view multi-behavior interest learning module that consists of two submodules: the behavior difference aware submodule, which captures intra-behavior interests for each behavior type and the correlations between various types of behaviors, and the behavior commonality aware submodule, which captures the information of interest commonalities between various types of behaviors. Additionally, a multi-view contrastive learning module is proposed to conduct node self-discrimination, ensuring the authenticity of information integration among various types of behaviors, and facilitating an effective fusion of interest differences and interest commonalities. Experimental results on three real-world benchmark datasets demonstrate the effectiveness of MMNCL and its advantages over other state-of-the-art recommendation models. Our code is available at https://github.com/sujieyang/MMNCL.

Multi-view graph contrastive learning for social recommendation.

With the rapid popularity of online social media, recommendation systems have increasingly harnessed social relations to enhance user-item interactions and mitigate the data sparsity issue. Beyond social connections, the semantic relatedness among items has emerged as a crucial factor in comprehending their inherent connections. In this work, we propose a novel Multi-view Contrastive learning framework for Social Recommendation, named MultiCSR. This framework adaptively incorporates user social networks and item knowledge graphs into modeling users preferences within recommendation systems. To facilitate the alignment of different views, we introduce a dedicated multi-view contrastive learning process that extracts rich information from each view and foster mutual enhancement. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of our framework over representative recommendation methods. Furthermore, ablation studies offer a deeper understanding of the mechanisms underlying our framework.

PageRank Algorithm-Based Recommendation System for Construction Safety Guidelines

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The PageRank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on “High-rise Building Construction (General) Safety and Health Guidelines”, using ‘scaffolding’ as the keyword, demonstrates the framework’s effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as ‘fall’, ‘drop’, and ‘scaffolding’, with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management.

ANAGL: A Noise-Resistant and Anti-Sparse Graph Learning for Micro-Video Recommendation

In recent years, graph convolutional networks (GCNs) have seen widespread utilization within micro-video recommendation systems, facilitating the understanding of user preferences through interactions with micro-videos. Despite the commendable performance exhibited by GCN-based methodologies, several persistent issues demand further scrutiny. Primarily, most user-micro-video interactions involve implicit behaviors, such as clicks or abstentions, which may inadvertently capture irrelevant micro-video content, thereby introducing significant noise (false touches, low watch-ratio, low ratings) into users’ histories. Consequently, this noise undermines the efficacy of micro-video recommendations. Moreover, the abundance of micro-videos has resulted in fewer interactions between users and micro-video content. To tackle these challenges, we propose a noise-resistant and anti-sparse graph learning framework for micro-video recommendation. Initially, we construct a denoiser that leverages implicit multi-attribute information (e.g., watch-ratio, timestamp, ratings, and so on) to filter noisy data from user interaction histories. This process yields high-fidelity micro-video information, enabling a more precise modeling of users’ feature preferences. Subsequently, we employ a multi-view reconstruction approach and utilize cross-view self-supervised learning to gain insights into user and micro-video features. This strategic approach effectively mitigates the issue of data sparsity. Extensive experiments conducted on two publicly available micro-video recommendation datasets validate the effectiveness of our proposed method. For in-depth details and access to the code, please refer to our repository at “ https://github.com/kbk12/ANAGL.git .”

Reputation-Driven Asynchronous Federated Learning for Optimizing Communication Efficiency in Big Data Labeling Systems

With the continuous improvement of the performance of artificial intelligence and neural networks, a new type of computing architecture-edge computing, came into being. However, when the scale of hybrid intelligent edge systems expands, there are redundant communications between the node and the parameter server; the cost of these redundant communications cannot be ignored. This paper proposes a reputation-based asynchronous model update scheme and formulates the federated learning scheme as an optimization problem. First, the explainable reputation consensus mechanism for hybrid intelligent labeling systems communication is proposed. Then, during the process of local intelligent data annotation, significant challenges in consistency, personalization, and privacy protection posed by the federated recommendation system prompted the development of a novel federated recommendation framework utilizing a graph neural network. Additionally, the method of information interaction model fusion was adopted to address data heterogeneity and enhance the uniformity of distributed intelligent annotation. Furthermore, to mitigate communication delays and overhead, an asynchronous federated learning mechanism was devised based on the proposed reputation consensus mechanism. This mechanism leverages deep reinforcement learning to optimize the selection of participating nodes, aiming to maximize system utility and streamline data sharing efficiency. Lastly, integrating the learned models into blockchain technology and conducting validation ensures the reliability and security of shared data. Numerical findings underscore that the proposed federated learning scheme achieves higher learning accuracy and enhances communication efficiency.

Cascading graph contrastive learning for multi-behavior recommendation

Traditional recommendation techniques often prioritize target behavior in practical recommendation scenarios(e.g., follow, play and buy). However, these approaches suffer from data sparsity issues and may not fully capture user’s personal preferences. To address this deficiency, multi-behavior recommendation technology has emerged, leveraging users’ multi-behavioral interactions for recommendation. Nevertheless, certain multi-behavior recommendation methods learning behavioral information from each behavior separately and then aggregate them before making recommendation, which inadvertently neglects the intrinsic connections between different behaviors. In some scenarios, user behavior often occurs in a fixed order, such as view -> cart -> buy in e-commerce platforms. In this work, we propose a novel Cascading Graph Constrastive Learning (CGCL) framework for Multi-Behavior recommendation. Specifically, we devise a graph contrastive learning block to learn distinctive user behavioral representations for each type of interaction. Leveraging the recommendation task, we aim to capture user preferences, while the contrastive learning provides supplementary supervisory signals to refine the user and item representation. By acknowledging the sequential order of behaviors, we utilize the cascading structure within our model to iteratively propagate and purify the personalized preferences of users. Extensive experimental results and ablation studies on three real-world datasets have shown that our CGCL framework outperforms various state-of-the-art recommendation methods and validated the effectiveness of our approach.

Adaptive denoising graph contrastive learning with memory graph attention for recommendation

Graph contrastive learning has emerged as a powerful technique for dealing with graph noise and mining latent information in networks, that has been widely applied in GNN-based collaborative filtering. Traditional graph contrastive learning methods commonly generate multiple augmented views, and then learn node representations by maximizing the consistency between these views. However, on one hand, manual view construction methods necessitate expert knowledge and a trial-and-error process. On the other hand, adaptive view construction methods require decoders which results in increased training costs. To address the aforementioned limitations, in this paper, we propose the Adaptive Denoising Graph Contrastive Learning with Memory Graph Attention for Recommendation (ADGA) framework. Firstly, we introduce the memory graph attention mechanism to capture node attention during multi-hop information aggregation. Then, unlike previous methods that required additional node representations to generate views, ADGA proposes, for the first time, directly using attention to adaptively generate structure-aware contrastive learning views. It reduces the training cost of the model and improves the cross-view consistency of node representations, that offers a new paradigm for adaptive graph contrastive learning. Experimental results on three real-world datasets demonstrate that ADGA achieves state-of-the-art performance in recommendation tasks. The code is available at https://github.com/Andrewsama/ADGA.

A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation

A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation

Enhanced side information fusion framework for sequential recommendation

Enhanced side information fusion framework for sequential recommendation

Multiobjective Optimization Strategy for Enhancing the Efficiency and Quality of Organic Thin-Film Manufacturing with Electrohydrodynamic Atomization Coating.

Electrohydrodynamic atomization coating technology is well-suited for micro-/nanoscale thin-film additive manufacturing. However, there are still some challenges in quality control and parameter adjustment during the coating process. Especially when coating on nonconductive and nonhydrophilic substrates, film quality and thickness uniformity are difficult to control. This paper proposes an optimization strategy for enhancing the efficiency and quality of thin-film manufacturing on nonconductive, nonhydrophilic glass substrates. In this paper, a visual inspection system was developed for in situ inspection and identification of droplet deposition states in the substrate surface. Then, the statistical relationship between the operating parameters and the quality of the deposition state was analyzed by response surface methodology. On this basis, machine learning models and intelligent recommendation frameworks for small data sets were developed to rapidly optimize operating parameters and improve the quality of thin-film coating. Optimization strategy developed by applying the principles of statistical modeling, analysis of variance, and global optimization are more efficient and less costly than traditional parameter screening methods. The experimental results show that optimum deposition quality can be obtained with the recommended operating parameters. And, validation results show a 12.8% improvement in film thickness uniformity. At the same time, no mura defects appeared on the thin-film surface. The proposed optimization strategy can improve the efficiency and quality of additive manufacturing of micro and nano thin films and is beneficial for advancing industrial applications of the electrohydrodynamic atomization coating.

Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data

This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value.

Improving Clinical Decision Making With a Two-Stage Recommender System.

Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses challenges due to the multifaceted nature of clinical data and the demand for tailored recommendations. In this article, we introduce a 2-Stage Recommendation framework for clinical decision-making, which leverages a publicly accessible dataset of electronic health records. In the first stage, a deep neural network-based model is employed to extract a set of candidate items, such as diagnoses, medications, and prescriptions, from a patient's electronic health records. Subsequently, the second stage utilizes a deep learning model to rank and pinpoint the most relevant items for healthcare providers. Both retriever and ranker are based on pre-trained transformer models that are stacked together as a pipeline. To validate our model, we compared its performance against several baseline models using different evaluation metrics. The results reveal that our proposed model attains a performance gain of approximately 12.3% macro-average F1 compared to the second best performing baseline. Qualitative analysis across various dimensions also confirms the model's high performance. Furthermore, we discuss challenges like data availability, privacy concerns, and shed light on future exploration in this domain.

A feature-enhanced knowledge graph neural network for machine learning method recommendation.

Large amounts of machine learning methods with condensed names bring great challenges for researchers to select a suitable approach for a target dataset in the area of academic research. Although the graph neural networks based on the knowledge graph have been proven helpful in recommending a machine learning method for a given dataset, the issues of inadequate entity representation and over-smoothing of embeddings still need to be addressed. This article proposes a recommendation framework that integrates the feature-enhanced graph neural network and an anti-smoothing aggregation network. In the proposed framework, in addition to utilizing the textual description information of the target entities, each node is enhanced through its neighborhood information before participating in the higher-order propagation process. In addition, an anti-smoothing aggregation network is designed to reduce the influence of central nodes in each information aggregation by an exponential decay function. Extensive experiments on the public dataset demonstrate that the proposed approach exhibits substantial advantages over the strong baselines in recommendation tasks.

Interactive precision medicine revolution: unleashing a deep learning framework for drug response and recommendation

Interactive precision medicine revolution: unleashing a deep learning framework for drug response and recommendation