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In response to the COVID-19 crisis, higher education institutions increasingly rely on e-learning systems. Indeed, the higher education market has become increasingly competitive with the addition of open education models. However, the abundance of accessible online courses makes it challenging to deliver education that meets student needs. Learners have diverse profiles based on their traits, motivations, prior knowledge, and learning preferences. Recently, much research has given attention to the importance of using the contextual parameters to perform more accurate recommendations. In this context, context-aware recommendation of pedagogical resources can deal with the issue of information overload, cold start problem and meeting the learner's preferences. This paper describes a context-aware recommender system that harness the learner's contextual information. Our proposed approach is called Context Embedding Deep Collaborative Filtering (CEDCF), which enriches the learner profile with extracted sentiments from previous interactions. The proposed approach comprises three models, called Generalized Matrix Factorzation (GMF) , Multilayer Perceptron (MLP) and Neural Matrix Factorization (NeuMF). The GMF and the MLP are respectively applied to the rating matrix and the contextual parameters. The outputs of these models are then fed into a neural network to perform rating prediction. To put our proposal into shape, we model a real-world application of a merged coursera dataset to recommend courses. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Implementing effective content moderation systems at scale is an unavoidable and complex challenge facing technology platforms. Developing systems that automate detection and removal of violative content is fraught with performance, safety and fairness considerations that make their implementation challenging. In particular, content-based systems require large amounts of data to train, cannot be easily transferred between contexts, and are susceptible to data drift. For these reasons, platforms employ a wide range of content classification models and rely heavily on human moderation, which can be prohibitively expensive to implement at scale. To address some of these challenges, we developed a framework that relies on consumption patterns to find high-quality leads for human reviewers to assess. This framework leverages consumption networks, and ranks candidate items for review using two techniques: Mean Percentile Ranking (MPR), which we have developed, and an adaptation of Label Propagation (LP). We demonstrate the effectiveness of this approach to find violative material in production settings using professional reviewers, and on a publicly available dataset from MovieLens. We compare our results with a popular collaborative filtering (CF) baseline, and we show that our approach outperforms CF in production settings. Then, we explore how performance can improve using Active Learning techniques. The key advantage of our approach is that it does not require any content-based data; it is able to find both low- and high-consumption items, and is easily scalable and cost effective to run. [ABSTRACT FROM AUTHOR]

The general personality traits, notably the Big-Five personality traits, have been increasingly integrated into recommendation systems. The personality-aware recommendations, which incorporate human personality into recommendation systems, have shown promising results in general recommendation areas including music, movie, and e-commerce recommendations. On the other hand, the number of research delving into the applicability of personality-aware recommendations in specialized domains such as finance and education remains limited. In addition, these domains have unique challenges in incorporating personality-aware recommendations as domain-specific psychological traits such as risk tolerance and behavioral biases play a crucial role in explaining user behavior in these domains. Addressing these challenges, this study addresses an in-depth exploration of personality-aware recommendations in the financial domain, specifically within the context of stock recommendations. First, this study investigates the benefits of deploying general personality traits in stock recommendations through the integration of personality-aware recommendations with user-based collaborative filtering approaches. Second, this study further verifies whether incorporating domain-specific psychological traits along with general personality traits enhances the performance of stock recommender systems. Thirdly, this paper introduces a personalized stock recommendation model that incorporates both general personality traits and domain-specific psychological traits as well as transaction data. The experimental results show that the proposed model outperformed baseline models in financial stock recommendations. [ABSTRACT FROM AUTHOR]

The article focuses on the Cluster-based Graph Collaborative Filtering (ClusterGCF), a novel recommendation model designed to enhance representation learning by addressing the challenges of high-order neighboring nodes and user interests. Topics include the introduction of a soft node clustering method that groups users and items, the construction of cluster-specific graphs to filter out noise and capture information and ClusterGCF's state-of-the-art performance across multiple datasets.

Recommender systems are currently a suitable alternative for providing easy and appropriate access to information for users in today's digital information-overloaded world. However, an important drawback of these systems is the inconsistent behavior of users in providing item preferences. To address this issue, several natural noise management (NNM) approaches have been proposed, which positively influence recommendation accuracy. However, a major limitation of such previous works is the disregarding of the time-related information coupled to the rating data in RSs. Based on this motivation, this paper proposes two novel methods, named SeqNNM and SeqNNM-p for NNM focused on an incremental, time-aware recommender system scenario that has not yet been considered, by performing a classification-based NNM over specific preference sequences, driven by their associated timestamps. Such methods have been evaluated by simulating a real-time scenario and using metrics such as mean absolute error, root-mean-square error, precision, recall, NDCG, number of modified ratings, and running time. The obtained experimental results show that in the used settings, it is possible to achieve better recommendation accuracy with a low intrusion degree. Furthermore, the main innovation associated with the overall contribution is the screening of natural noise management approaches to be used on specific preferences subsets, and not over the whole dataset as discussed by previous authors. These proposed approaches allow the use of natural noise management in large datasets, in which it would be very difficult to correct the entire data. [ABSTRACT FROM AUTHOR]

In today's digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system's efficacy. [ABSTRACT FROM AUTHOR]

Recommendation systems are crucial in boosting companies' revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users' and items' content data, and second, a traditional recommendation system that employs users' past behaviour data. We introduce a novel deep learning-based recommendation system called convolutional autoencoder recommendation system (CAERS). It uses a convolutional autoencoder (CAE) to capture high-order meaningful relationships between users' and items' content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users' past behaviour data, utilizing singular value decomposition (SVD). Finally, in the last step, we combine the two method's predictions with linear regression. We determine the optimal weight for each prediction generated by the collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Across both datasets, the hybrid CAERS-CF model demonstrates an average RMSE improvement of approximately 3.70% and an average MAE improvement of approximately 5.96% compared to the next best models. [ABSTRACT FROM AUTHOR]

This research presents a new Demographic-Enhanced Cosine-KNN method for collaborative filtering in recommender systems. Our method demonstrates superior performance compared to state-of-the-art techniques across various datasets, indicating substantial enhancements in recommendation accuracy. Assessments of the MovieLens 100K and 1M datasets demonstrate significant improvements in RMSE and MAE metrics relative to traditional KNN-Basic and advanced ExtKNNCF algorithms. The proposed method demonstrates improvements of up to 17.1% in RMSE and 14.4% in MAE compared to KNN-Basic, while consistently exceeding ExtKNNCF by margins ranging from 2.0% to 10.1%. Our method demonstrates significant improvement compared to the standard Cosine-KNN approach, achieving enhancements of 1.9% in RMSE and 2.4% in MAE for the 100K dataset, and 0.7% in RMSE and 1.9% in MAE for the 1M dataset. The consistent gains observed across various sample sizes indicate the stability and scalability of the strategy employed. The results highlight the efficacy of our demographic-enhanced strategy in overcoming the limitations of current collaborative filtering methods, providing a scalable and robust solution for enhancing recommendation accuracy across various application contexts. [ABSTRACT FROM AUTHOR]

Aiming at the problems of sparse data, cold start, timeliness and privacy protection in current recommendation systems, a collaborative filtering recommendation algorithm based on density weight and improved similarity was proposed. The collaborative filtering recommendation algorithm, which combines differential privacy protection clustering and improved similarity, aims to improve the accuracy of the recommendation system and ensure the privacy security of user data. The user-project score matrix was constructed through data pre-processing, and the Weight Slope One algorithm was used to fill empty values in an intelligent way. The DWDPK-medoids privacy clustering algorithm was used to cluster the matrix accurately, and the time factor and user interest preference factors were integrated to change the calculation of similarity, thus improving the relevance of recommendation. Finally, the target user's rating of the project was predicted. Comparative experiments were conducted on the Movie Lens dataset against five privacy recommendation algorithms proposed by current scholars validate the efficacy of the proposed algorithm, showing reductions in evaluation metrics such as root mean squared error (RMSE) and mean absolute error (MAE). This indicates that the method partially addresses issues such as data sparsity, cold start, and timeliness, while enhancing recommendation accuracy on the basis of protecting user privacy. [ABSTRACT FROM AUTHOR]

In the era of vast information, individuals are immersed in choices when purchasing goods and services. Recommender systems (RS) have emerged as vital tools to navigate these excess options. However, these systems encounter challenges like data sparsity, impairing their effectiveness. This paper proposes a novel approach to address this issue and enhance RS performance. By integrating user demographic data, singular value decomposition (SVD) clustering, and semantic similarity in collaborative filtering (CF), we introduce the UDIS method. This method amalgamates four prediction types—user-based CF (U), demographic-similarity-based (D), item-based CF (I), and semantic-similarity-based (S). UDIS generates separate predictions for each category and evaluates four different merging techniques—the average, max, weighted sum, and Shambour methods—to integrate these predictions. Among these, the average method proved most effective, offering a balanced approach that significantly improved precision and accuracy on the MovieLens dataset compared to alternative methods. [ABSTRACT FROM AUTHOR]

Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based methods often encounter biases due to imbalances in training data. This phenomenon makes CF-based methods tend to prioritize recommending popular items and performing unsatisfactorily on inactive users. Existing works address this issue by rebalancing training samples, reranking recommendation results, or making the modeling process robust to the bias. Despite their effectiveness, these approaches can compromise accuracy or be sensitive to weighting strategies, making them challenging to train. Therefore, exploring how to mitigate these biases remains in urgent demand. In this article, we deeply analyze the causes and effects of the biases and propose a framework to alleviate biases in recommendation from the perspective of representation distribution, namely Group-Alignment and Global-Uniformity Enhanced Representation Learning for Debiasing Recommendation (AURL). Specifically, we identify two significant problems in the representation distribution of users and items, namely group-discrepancy and global-collapse. These two problems directly lead to biases in the recommendation results. To this end, we propose two simple but effective regularizers in the representation space, respectively named group-alignment and global-uniformity. The goal of group-alignment is to bring the representation distribution of long-tail entities closer to that of popular entities, while global-uniformity aims to preserve the information of entities as much as possible by evenly distributing representations. Our method directly optimizes both the group-alignment and global-uniformity regularization terms to mitigate recommendation biases. Please note that AURL applies to arbitrary CF-based recommendation backbones. Extensive experiments on three real datasets and various recommendation backbones verify the superiority of our proposed framework. The results show that AURL not only outperforms existing debiasing models in mitigating biases but also improves recommendation performance to some extent. [ABSTRACT FROM AUTHOR]

Logistics is crucial to the global economy, but traditional logistics management models have issues such as low informationization, single structures, low efficiency, and insufficient recommendation accuracy. This study aims to improve personalized recommendation in logistics management using the collaborative filtering algorithm and comparing it with Logistic Regression (LR) and Factorization Machine (FM) algorithms. The modified cosine similarity calculation method had the lowest absolute error at 0.91. After improving the algorithm, the PR value increased to 0.9952, showing a better balance between accuracy and recall. Combining the improved collaborative filtering recommendation algorithm with the actual logistics management model resulted in higher accuracy in personalized recommendations to users. The accuracy also increased with the number of recommended companies. Overall, the improved algorithm showed promising effectiveness and feasibility in the context of logistics management. [ABSTRACT FROM AUTHOR]

One of the common techniques to reduce the scalability problem in collaborative filtering (CF)-based recommender systems is the clustering technique, which accelerates finding the nearest neighbor users in the recommendation process. Different clustering algorithms lead to improved accuracy and diversity in recommender systems. It is challenging to develop recommender systems based on clustering with decreasing scalability and simultaneously increasing accuracy. This article proposes a new clustering-based recommender system that takes into account the theoretical properties of the Learning Automata (LA) technique. The presented clustering novelty lies in the fact that employing LA technique for the user clustering in a CF-based recommender system has not been set forth so far in a way that addresses the scalability while improving accuracy. In addition, a novel similarity metric is embedded in the proposed algorithm to measure the similarity value between users. This metric is developed as like/dislike (LD) which can significantly improve the accuracy by reducing the computational cost. Extensive simulations have been performed on real-world datasets such as MovieLens and FilmTrust, which confirm the effectiveness of the proposed algorithm. In this regard, the proposed algorithm has improved the precision between 5 and 16% on average compared to the existing state-of-the-art methods such as GA-GELS, NUSCCF, NNMF, and KL-KM. [ABSTRACT FROM AUTHOR]

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI). [ABSTRACT FROM AUTHOR]

The evolution of recommendation systems has revolutionized user experiences by providing personalized recommendations. Although conventional systems such as collaborative and content-based filtering are reliable, they still suffer from inherent limitations. We introduce a hybrid recommendation system that combines content-based filtering using TF-IDF and cosine similarity with collaborative filtering and SVD to address these challenges. We bolster our model through supervised machine learning algorithms like decision trees (DT), random forests (RF), and support vector regression (SVR). To amplify predictive prowess, boosting algorithms including CatBoost and XGBoost are harnessed. Our experiments are performed on the benchmark dataset MovieLens 1M, which highlights the superiority of our hybrid method over more traditional alternatives with SVR being the best-performing algorithm consistently. Our hybrid model achieved an MSLE score of 2.3 and an RMSLE score of 1.5, making SVR consistently the best-performing algorithm in the recommendation system. This combination demonstrates the potential of collaborative-content hybrids supported by cutting-edge machine-learning techniques to reshape the field of recommendation systems. [ABSTRACT FROM AUTHOR]

The rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers' travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers' travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The recommendation system is a filtering system. It filters a collection of things based on the historical behavior of a user, it also tries to make predictions based on user preferences and make recommendations that interest customers. While incredibly useful, they can face various challenges affecting their performance and utility. Some common problems are, for example, when the number of users and items grows, the computational complexity of generating recommendations increases, which can increase the accuracy and precision of recommendations. So, for this purpose and to improve recommendation system results, we propose a recommendation system combining the demographic approach with collaborative filtering, our approach is based on users' demographic information such as gender, age, zip code, occupation, and historical ratings of the users. We cluster the users based on their demographic data using the k-means algorithm and then apply collaborative filtering to the specific user cluster for recommendations. The proposed approach improves the results of the collaborative filtering recommendation system in terms of precision and recommends diverse items to users. [ABSTRACT FROM AUTHOR]

There should be some improvements to increase the performance of Metaverse investments. However, businesses need to focus on the most important actions to provide cost effectiveness in this process. In summary, a new study is needed in which a priority analysis is made for the performance indicators of Metaverse investments. Accordingly, this study aims to evaluate the main determinants of the performance of the metaverse investments. Within this context, a novel model is created that has four different stages. The first stage is related to the prioritizing the experts with artificial intelligence-based decision-making method. Secondly, missing evaluations are estimated by expert recommendation system. Thirdly, the criteria are weighted with Quantum picture fuzzy rough sets-based (QPFR) M-Step-wise Weight Assessment Ratio Analysis (SWARA). Finally, investment decision-making priorities are ranked by QPFR VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The main contribution of this study is the integration of the artificial intelligence methodology to the fuzzy decision-making approach for the purpose of computing the weights of the decision makers. Owing to this condition, the evaluations of these people are examined according to their qualifications. This situation has a positive contribution to make more effective evaluations. Organizational effectiveness is found to be the most important factor in improving the performance of metaverse investments. Similarly, it is also identified that it is important for businesses to ensure technological improvements in the development of Metaverse investments. On the other side, the ranking results indicate that regulatory framework is the most critical alternative in this regard. [ABSTRACT FROM AUTHOR]

In recommender systems, the user items are offered tailored to users' requirements. Because there are multiple cloud services, recommending a suitable service for users' requirements is of paramount importance. Cloud recommender systems are qualified depending on the extent to which they accurately predict service quality values. Because no service was chosen by the user beforehand, and no record of the user's selections is available, it became challenging to recommend it to users. To promote the recommender system quality, to accurately predict service quality values by offering various procedures, including collaborative filtering, matrix factorization, and clustering. This review article first mentions the general problem and states the need for research, followed by examining and expressing the kinds of recommender systems along with their problems and challenges. In the present review, various approaches, platforms, and solutions are reviewed to articulate the pros and cons of individual approaches, simulation models, and evaluation metrics employed in the reviewed techniques. The measured values in various approaches of the papers are compared with one another in several diagrams. This review paper reviews and introduces the entire datasets applied in the studies. [ABSTRACT FROM AUTHOR]

In order to overcome the limitation of the cold start problem in the recommendation process on the performance of new users or new project scenarios, a meta-learning based graph neural network cold start recommendation model, namely MetaNGCF, is proposed to improve the accuracy and diversity of user-to-recommendation. Firstly, a perceptual meta-learning structure with adaptive properties is proposed to construct a model with a hybrid user-project interaction graph and neural graph, which expresses user behavior and project knowledge in a unified way. This structure incorporates an adaptive weighted loss strategy to correct the meta-learning paths in real time, in order to avoid the damage caused by noisy tasks on the model. Secondly, a clustering algorithm is applied to transform the high-dimensional feature space into a low-dimensional low-rank feature space. User preference learning is utilized to task aggregation layer gradient to encode the collaborative signals and automatically generalize the higher-rank connectivity between users and projects, which in turn captures the NGCF general knowledge semantics. Finally, the results are validated in comparison with existing MetaHIN algorithm, and the results show that MetaNGCF has better performance on Recall@20 and NDCG@20. [ABSTRACT FROM AUTHOR]

Memory-based collaborative filtering schemes are among the most effective recommendation technologies in terms of prediction quality, despite commonly facing issues related to accuracy, scalability, and privacy. A prominent approach suggests an intuitively reasonable modification to the similarity function, which has been proven to provide more accurate recommendations than those generated by state-of-the-art memory-based collaborative filtering methods. However, this scheme exacerbates the scalability problem due to additional computational costs and fails to protect individual privacy. In this study, we recommend using a preprocessing method to eliminate relatively dissimilar items from the prediction estimation process, thereby enhancing the scalability of the proposed approach. We explore how to provide recommendations based on the previously proposed similarity function while preserving privacy and propose privacy-preserving schemes to accomplish this task. Additionally, we apply our preprocessing approach to our proposed privacy-preserving schemes to improve both scalability and accuracy. After analyzing our schemes with respect to privacy and additional costs, we conduct experiments with real data to examine the impact of our schemes on scalability and accuracy. The empirical outcomes indicate that our preprocessing scheme significantly alleviates scalability issues in both conventional and privacy-preserving environments and enhances accuracy within privacy-preserving frameworks. [ABSTRACT FROM AUTHOR]

Enterprise human resources management faces problems such as improper service matching and inadequate management. In order to solve the problems in traditional human resource management, an intelligent human resource management platform is designed based on the Spring Cloud framework. Firstly, for the problem of insufficient human resource matching, an improved hybrid genetic algorithm based human resource recommendation model is proposed, which ranks job seekers through collaborative filtering. At the same time, the improved hybrid genetic algorithm is used to optimize the ranking weight and improve content recommendation. A human resource scheduling method considering resource balance is proposed for work scheduling problems, and scheduling solutions are solved using branch definition method and heuristic method. In the experimental analysis of human resources recommendation, the root mean square error and mean absolute error of the proposed model were the lowest among the three, which were 0.013 and 0.07 3 respectively. In the comparison of recommendation accuracy, the proposed model performed best in both management positions and service positions. The recommendation accuracy was 0.982 and 0.976 respectively, which was better than other models. In the experimental analysis of manpower scheduling, the designed model has the best optimization results among the three scheduling models. Finally, the scheduling solution for Project A is carried out. Both solutions can reduce the project duration and meet the needs of the enterprise. The research content provides important technical support for the scheduling management of enterprise human resources and information construction. [ABSTRACT FROM AUTHOR]

This project aims to develop a recommendation system to mitigate looping issues in HDD slider testing using the Amber testing machine (Machine A). Components simulating the HDD often fail and require repair before re-testing. However, post-repair, there is a 34% probability that the component (referred to as Product A) will experience looping, characterized by repeated failures with error code A. This recurring issue significantly hampers testing efficiency by reducing the number of successful slider tests. To address this challenge, we propose a dual-approach recommendation system that provides technicians with actionable insights to minimize the occurrence of looping. For previously analyzed components, a collaborative filtering technique utilizing implicit ratings is employed to generate recommendations. For new components, for which prior data are unavailable, a cosine similarity approach is applied to suggest optimal actions. An automatic training system is implemented to retrain the model as new data become available, ensuring that the recommendation system remains robust and effective over time. The proposed system is expected to offer precise guidance to technicians, thereby improving the overall efficiency of the testing process by reducing the frequency of looping issues. This work represents a significant advancement in enhancing operational reliability and productivity in HDD slider testing. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it requires proper modification since the data are sequential, the user feedback is distributed among devices, and the transmission of users' data to aggregate common patterns must be protected against leakage. According to such requirements, we modify the structure of the classical matrix factorization model and update the training procedure to sequential learning. Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model. One more ingredient of our approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server. To demonstrate the efficiency of the proposed model, we use publicly available mobile user behavior data. We compare our model with sequential rules and models based on the frequency of app launches. The comparison is conducted in static and dynamic environments. The static environment evaluates how our model processes sequential data compared to competitors. The dynamic environment emulates the real-world scenario, where users generate new data by running apps on devices. Our experiments show that the proposed model provides comparable quality with other methods in the static environment. However, more importantly, our method achieves a better privacy-utility trade-off than competitors in the dynamic environment, which provides more accurate simulations of real-world usage. [ABSTRACT FROM AUTHOR]

Recommender systems are already being used by several biggest e-commerce websites to assist users in finding things to buy. A recommender system gains knowledge from a consumer and suggests goods from the available goods that will find most value. In this deep learning technique, the Hybrid Deep Collaborative Transformer (HDCT) method has emerged as a promising approach. However, it is crucial to thoroughly examine and rectify any potential errors or limitations in the optimization process to ensure the optimal performance of the HDCT model. This study aims to address this concern by thoroughly evaluating the HDCT method uncovering any underlying errors or shortcomings. By comparing its performance against other existing models, the proposed HDCT with Federated Learning method demonstrates superior recommendation accuracy and effectiveness. Through a comprehensive analysis, this research identifies and rectifies the errors in the HDCT model, thereby enhancing its overall performance. The findings of this study provide valuable insights for researchers and practitioners in the field of e-commerce recommendation systems. Data for the RS is collected from the Myntra fashion product dataset. By understanding and addressing the limitations of the HDCT method, businesses can leverage its advantages to improve customer satisfaction and boost their revenue. Ultimately, this research contributes to the ongoing advancements in e-commerce recommendation systems and paves the way for future improvements in this rapidly evolving domain. The suggested model's efficacy is assessed using metrics for MSE, MSRE, NMSE, RMSE, and MAPE. The suggested values in metrics are 0.2971, 0.2763, 0.4013, 0.3222, 0.2911 at a 70% learn rate and 0.2403, 0.2234, 0.3506, 0.2025, 0.2597 at an 80% learn rate, and the proposed model outperformed with the least amount of error. [ABSTRACT FROM AUTHOR]

Recommendation has become especially crucial during the COVID-19 pandemic as a significant number of people rely on online shopping from home. Existing recommendation algorithms, designed to address issues like cold start and data sparsity, often overlook the time constraints of users. Specifically, users expect to receive recommendations for products of interest in the shortest possible time. To address this challenge, we propose a novel collaborative filtering recommendation algorithm that leverages the advantages of quantum computing circuits based on data reconstruction. This approach allows for the rapid identification of users similar to the target user, thereby improving recommendation speed. In our method, we utilize the information of known users to linearly reconstruct that of the target users, forming a relational matrix. Subsequently, we employ \(l_{2,1}-\) norm and \(l_{1}-\) norm to sparsely constrain the relationship matrix, deducing the weight of each known user. The final step involves providing similar recommendations to target users based on these weights. Furthermore, we implement the proposed algorithm using a quantum circuit, enabling exponential acceleration. The final weight matrix is derived from the quantum state outputted by the circuit. The speed of this process is theoretically demonstrated in detail. Experimental results indicate that our algorithm outperforms state-of-the-art methods in terms of root mean squared error (RMSE), mean absolute error (MAE) and normalized discounted cumulative gain (NDCG). Compared to state-of-the-art comparison algorithms, the proposed algorithm achieves the fastest recommendation speed across eight public datasets. [ABSTRACT FROM AUTHOR]

Graph Collaborative Filtering is a widely adopted approach for recommendation, which captures similar behavior features through Graph Neural Network (GNN). Recently, Contrastive Learning (CL) has been demonstrated as an effective method to enhance the performance of graph collaborative filtering. Typically, CL-based methods first perturb users' history behavior data (e.g., drop clicked items), then construct a self-discriminating task for behavior representations under different random perturbations. However, for widely existing inactive users, random perturbation makes their sparse behavior information more incomplete, thereby harming the behavior feature extraction. To tackle the above issue, we design a novel directional perturbation-based CL method to improve the graph collaborative filtering performance. The idea is to perturb node representations through directionally enhancing behavior features. To do so, we propose a simple yet effective feedback mechanism, which fuses the representations of nodes based on behavior similarity. Then, to avoid irrelevant behavior preferences introduced by the feedback mechanism, we construct a behavior self-contrast task before and after feedback, to align the node representations between the final output and the first layer of GNN. Different from the widely adopted self-discriminating task, the behavior self-contrast task avoids complex message propagation on different perturbed graphs, which is more efficient than previous methods. Extensive experiments on three public datasets demonstrate that the proposed method has distinct advantages over other CL methods on recommendation accuracy. [ABSTRACT FROM AUTHOR]

The subject of personalized exercise recommendation holds significant relevance within the domain of personalized services in smart education. Nevertheless, traditional algorithms have often lacked a deep understanding of student characteristics and failed to adequately explore the relationship between knowledge mastery and questionanswering behaviors, leading to low recommendation accuracy. To address these issues, combining the knowledge tracing machine and the user-based collaborative filtering algorithm, as a KTM-based multi-feature fusion exercise recommendation model, SKT-MFER was proposed. Firstly, as a knowledge tracking model, KTM-LC, incorporating student learning behaviors and learning abilities, was constructed to accurately assess the student's knowledge mastery level. Subsequently, two filters were implemented to ensure the exercise recommendation's accuracy: the first was an initial screening utilizing the knowledge point mastery matrix to eliminate students who were similar to the target student, and the second was a filtering process considering the combined similarity of cognitive state similarity and exercise difficulty similarity. Through extensive experiments, it proves that the proposed method yields better results than some existing baseline models. [ABSTRACT FROM AUTHOR]

Practitioner's Abstract Online platforms/retailers widely use collaborative filtering (CF)-based generic product recommendations to improve sales. These systems recommend products to a consumer based on the product co-views and co-purchases by other consumers on the website but do not leverage the consumer's browsing data. Based on a field study on a U.S. fashion apparel and home goods retailer's website, we show that informing generic CF recommendations to individual consumers' browsing history can generate substantial additional sales. Specifically, we show that it is optimal to offer generic CF recommendations to a consumer if the consumer has not carted a product and recommend products he or she has seen in the previous sessions (retargeted recommendations) if he or she has carted a product. Our simulation results show that such recommendations could result in a 3% increase in total sales compared with conventional generic CF recommendations. Online platforms/retailers with detailed consumer browsing data can implement such recommendations to achieve higher sales. Although the effects of algorithmic product recommendations on product sales are understood, the differential effects of retargeted recommendations (recommended products a user has previously viewed) versus generic recommendations (recommended products a user has not previously viewed) are unclear. We conduct a field experiment to empirically examine the relative effect of retargeted versus generic recommendations on product sales at different stages of users' purchase funnel. The product recommendations can affect sales by influencing the number of product impressions and their conversion rates (purchase probability conditional on impression). We separately estimate the effect of retargeted and generic recommendations on product impressions and conversion rates. We find that (i) generic recommendations increase conversion rates only in the early purchase funnel stage, but retargeted recommendations do not affect conversion rates, and (ii) both recommendations result in a higher number of impressions of recommended products. Overall, retargeted (generic) recommendations result in higher recommended and total product sales in the late (early) purchase funnel stage. We also conducted a controlled experiment on Amazon MTurk to unveil that retargeting (showing previously viewed products to users) drives the effect of retargeted recommendations. Our counterfactual simulations show that the retailer can obtain up to three percent higher product sales by applying our findings to the existing recommendation systems. Our research has implications for online retailers and the design of algorithmic product recommendation systems. History: Param Singh, Senior Editor; Idris Adjerid, Associate Editor. Funding: This work was supported by the Leavey School of Business at Santa Clara University [Grant 102720]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2020.0560. [ABSTRACT FROM AUTHOR]

Since online electronic books have become popular, book recommendation systems have been invented and challenged to handle the high demand from users in the digital era. This study aimed to develop and evaluate a book recommendation model using data mining techniques through RapidMiner Studio. The datasets used were comprised of 981,756 user ratings. Before conducting the data analytics, the data was pre-processed to eliminate duplicates and retain only the highest ratings. Collaborative Filtering (CF) techniques, particularly k-Nearest Neighbours (k-NN) and Matrix Factorization (KF), were employed to elicit insightful information for development and to highlight their capabilities in handling enormous datasets. Furthermore, statistical analysis, visualization, elementary modelling, and model combinations were investigated to compare their performance. To reinforce creditability, modelling techniques and parameter adjustments were integrated to optimize the performance of the algorithms, since the results indicated that different model settings and data partitions impacted the effectiveness of the recommendation system. Additionally, these results demonstrated the potential of hybrid models in improving the accuracy and efficiency of recommendation systems and highlighted the trade-off between algorithmic approaches and dataset characteristics that interplay in optimizing the performance of recommendation systems. [ABSTRACT FROM AUTHOR]

Abstract Implementing effective content moderation systems at scale is an unavoidable and complex challenge facing technology platforms. Developing systems that automate detection and removal of violative content is fraught with performance, safety and fairness considerations that make their implementation challenging. In particular, content-based systems require large amounts of data to train, cannot be easily transferred between contexts, and are susceptible to data drift. For these reasons, platforms employ a wide range of content classification models and rely heavily on human moderation, which can be prohibitively expensive to implement at scale. To address some of these challenges, we developed a framework that relies on consumption patterns to find high-quality leads for human reviewers to assess. This framework leverages consumption networks, and ranks candidate items for review using two techniques: Mean Percentile Ranking (MPR), which we have developed, and an adaptation of Label Propagation (LP). We demonstrate the effectiveness of this approach to find violative material in production settings using professional reviewers, and on a publicly available dataset from MovieLens. We compare our results with a popular collaborative filtering (CF) baseline, and we show that our approach outperforms CF in production settings. Then, we explore how performance can improve using Active Learning techniques. The key advantage of our approach is that it does not require any content-based data; it is able to find both low- and high-consumption items, and is easily scalable and cost effective to run.

Abstract The interaction data used for training recommender systems often exhibit a long-tail distribution. Such highly imbalanced data distribution results in an unfair learning process among items. Contrastive learning alleviates the above issue by data augmentation. However, it lacks consideration of the significant disparity in popularity between items and may even introduce false negatives during the data augmentation, misleading user preference prediction. To address this issue, we combine contrastive learning with a weighted model for negative validation. By penalizing identified false negatives during training, we limit their potential harm within the training process. Meanwhile, to tackle the scarcity of supervision signals for unpopular items, we design Popularity Associated Modeling to mine the correlation among items. Then we guide unpopular items to learn hidden features favored by specific users from their associated popular items, which provides effective supplementary information for their representation modeling. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms state-of-the-art baselines in recommendation performance, with Recall@20 improvements of 4.2%, 2.4% and 3.6% across the datasets, but also shows significant effectiveness in relieving popularity bias.

Abstract Exploring the optimization of communication strategies for animation films in the context of cross-cultural communication, this research integrates the Internet of Things (IoT) and convolutional networks. The research constructs a collaborative filtering (CF) movie recommendation model based on a graph convolutional neural network (GCN) and investigates its application in cross-cultural communication. The fusion of IoT and convolutional networks in movie communication is also analyzed, and the effectiveness of the proposed GCN-CF model is validated through comparative experiments. The results indicate that, compared to other models, the GCN-CF model achieves the lowest Root Mean Square Error (RMSE) on the MovieLens 100 K and MovieLens 1 M datasets, with values of 0.8762 and 0.8275, respectively. Compared to traditional models, the GCN-CF model exhibits significantly superior performance in terms of RMSE, with reductions ranging from 0.6 to 5.2%, highlighting its heightened detection accuracy and overall performance. Moreover, the performance of the GCN-CF model is enhanced after introducing attention mechanisms and auxiliary information on both datasets, showing an improvement of 0.4% compared to the scenario without these additions. This data demonstrates the effectiveness of attention mechanisms and auxiliary information. Finally, the research presents an animation film communication strategy based on IoT and convolutional networks, offering novel insights for film production and communication, along with positive implications for cultural exchange and the advancement of the global media industry.

Abstract The rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers’ travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers’ travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems.

E-commerce platforms often use collaborative filtering (CF) algorithms to recommend products to consumers. What recommendations consumers receive and how they respond to the recommendations largely depend on the design of CF algorithms. However, the extant empirical research on recommender systems has primarily focused on how the presence of recommendations affects product demand, without considering the underlying algorithm design. Leveraging a field experiment on a major e-commerce platform, we examine the differential impact of two widely used CF designs: view-also-view (VAV) and purchase-also-purchase (PAP). We found several striking differences between the impact of these two designs on individual products. First, VAV is about seven times more effective in generating additional product views than PAP but only about twice as effective in generating sales due to a lower conversion rate. Second, VAV is more effective in increasing views for more expensive products, whereas PAP is more effective in increasing the sales of cheaper products. Third, VAV is less effective in increasing the views but more effective in increasing the sales of products with higher purchase incidence rates (PIRs). Finally, when aggregated over all products with the same levels of price or PIRs, VAV dominates PAP in generating views and the difference is more striking for products with higher prices or lower PIRs. Interestingly, PAP is more effective than VAV in increasing the sales of products with low prices or moderate PIRs, though VAV generates more sales than PAP overall. Our findings suggest that platforms may benefit from employing different CF designs for different types of products. [ABSTRACT FROM AUTHOR]

Traditional collaborative filtering recommendation algorithms suffer from low recommendation efficiency and poor accuracy when calculating similarities between users or items. To address this issue and improve the efficiency of recommendation systems, the paper introduces an algorithm called K-nearest neighbors and non-negative matrix factorization (KNNCNMF) collaborative filtering recommendation algorithm. When calculating the similarity between users or items, the algorithm extracts the latent factors of users and items through matrix decomposition, constructs a low-dimensional dense “user–item factor” matrix, and inputs it into the classifier for rating prediction, which replaces the complex similarity calculation and further improves the efficiency of the user–item similarity calculation. We use performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Precision, and Recall to measure our method. The experimental results show that compared to other algorithms, our method improves the MAE metric by 1.78% on average, the RMSE metric by 4.48% on average, the Precision metric by 4.66% on average, and the Recall metric by 7.95% on average. It proves the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Within the realm of social recommendation, a recommender system can enhance its performance through the use of social information among users. Due to the abundance of redundant information in user interactions and social connections, it affects the performance of recommendation results negatively. Existing recommendation models do not distinguish the influence of different users and different friends. To solve this problem, this paper introduces a new recommendation framework, user preference and social relationship-aware light graph convolutional networks (USLGCN). The proposed framework distinguishes between users based on their interactions with items and social relationships to enhance recommendation accuracy. Specifically, we design a subgraph classification strategy that divides the user–item interaction graph and social graph into different subgraphs to capture the impact of various user types on items and friends, thereby reducing negative information and enhancing model resilience. On top of that, we also design a graph fusion module that enhances recommendation performance by fusing data from multiple subgraphs together. Experiments on public datasets show that USLGCN exhibits a 2.6% increase in recall accuracy compared to other social recommendation methods. [ABSTRACT FROM AUTHOR]

The interaction data used for training recommender systems often exhibit a long-tail distribution. Such highly imbalanced data distribution results in an unfair learning process among items. Contrastive learning alleviates the above issue by data augmentation. However, it lacks consideration of the significant disparity in popularity between items and may even introduce false negatives during the data augmentation, misleading user preference prediction. To address this issue, we combine contrastive learning with a weighted model for negative validation. By penalizing identified false negatives during training, we limit their potential harm within the training process. Meanwhile, to tackle the scarcity of supervision signals for unpopular items, we design Popularity Associated Modeling to mine the correlation among items. Then we guide unpopular items to learn hidden features favored by specific users from their associated popular items, which provides effective supplementary information for their representation modeling. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms state-of-the-art baselines in recommendation performance, with Recall@20 improvements of 4.2%, 2.4% and 3.6% across the datasets, but also shows significant effectiveness in relieving popularity bias. [ABSTRACT FROM AUTHOR]

Summary: When selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling‐based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL‐PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state‐of‐the‐art algorithms, and also shows good stability. [ABSTRACT FROM AUTHOR]