Recommendation Algorithm Research Articles

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the “user-item” latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students’ subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students’ self-directed learning efficiency.

Deep Attentive Interest Collaborative Filtering for Recommender Systems

Collaborative filtering (CF) is a pivotal building block in commercial recommender systems due to its strength and utility in user interest modeling. Recently, many researchers have turned to deep learning as a way to capture richer collaborative signals from user-item feature interactions. However, most deep-based methods only consider nonlinear, high-order interactions while ignoring the explicit collaborative signals in low-order interactions. They also typically ignore the quality of the user and item profiles. These are cornerstones in item recommendation that, we argue, must be considered for high-quality recommendations. Hence, we propose Deep Attentive Interest Collaborative Filtering (DAICF) to overcome these limitations. DAICF profiles users based on their interactive items, i.e., user neighborhood information. Similarly, item profiles are based on users who had interacted with it, i.e., item neighborhood information. Given a user's profile varies over different items, DAICF accurately models his attentive interests based on the specific target item. Low-order collaborative signals are captured by a shallow component, and high-order collaborative signals are captured by a deep component. These two complementary collaborative signals are then fused to provide rich recommendations that cut through today's information overload. By designing a personalized feature extraction method based on bilateral neighborhood information to solve the data sparsity problem in recommender systems, DAICF can dynamically distinguish the importance of a user's historical interaction items for predicting user preferences for a specific target item. A set of experiments against four real-world datasets validate that DAICF outperforms the most recent state-of-the-art recommendation algorithms and justifies the effectiveness and interpretability of our method.

A Multiinterest and Social Interest-Field Framework for Financial Security

Online payment has become an influential method of transaction. While improving convenience, this method of payment also brings great financial risks. Prevalidating transactions can be effective in reducing the number of frauds. For this reason, recommendation algorithms have been introduced to measure the credibility of transactions by predicting users’ ratings of items. However, most algorithms deal with the relationships in social networks without distinction, mixing positive and negative information into the recommender system, which brings huge noise. And, they only generate a single interest representation for each user to measure the similarity between users and spread interest, ignoring the diversity of user interests. Moreover, they did not consider the propagation of different interests would be different. In this article, we propose a multiinterest and social interest-field framework (MISIF) for social recommendations in financial security, which introduces capsule networks into social recommendation and extends the traditional single-interest representation to user multiinterest embedding by dynamic routing (DR) and other methods to improve the expressiveness of user embedding. After that, we construct social interest fields to integrate social interests based on multiinterest embedding, which alleviates the noise in social networks and user data sparsity problems. Finally, we aggregate user multiinterest embedding and additional information through neural networks to obtain the final prediction scores. Experiments with three publicly available datasets show that our proposed MISIF framework outperforms the state-of-the-art social recommendation methods.

A Multi-Population Based Evolutionary Algorithm for Many-Objective Recommendations

A Multi-Population Based Evolutionary Algorithm for Many-Objective Recommendations

Intelligent recommendation system for College English courses based on graph convolutional networks

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students’ major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.

Intelligent Talent Recommendation Algorithm for College Students for the Future Job Market

An intelligent talent recommendation algorithm is a sophisticated system that utilizes advanced data analysis techniques and machine learning algorithms to match individuals with suitable job opportunities or educational programs. By analyzing a wide range of data points such as skills, qualifications, work experience, and preferences, these algorithms can generate personalized recommendations tailored to each individual's unique profile and career goals. Moreover, intelligent talent recommendation algorithms continuously learn and improve over time, refining their recommendations based on user feedback and performance data. This paper presents an intelligent talent recommendation algorithm tailored for college students preparing for the future job market, utilizing Ranking Hidden Chain Deep Learning (RHC-DL). The algorithm aims to provide personalized recommendations by analysing students' skills, qualifications, interests, and career aspirations. Through simulated experiments and empirical validations, the efficacy of the RHC-DL-enhanced recommendation algorithm is evaluated. Results demonstrate significant improvements in recommendation accuracy and relevance compared to traditional methods. For instance, students using the RHC-DL algorithm reported a 40% increase in job offer acceptance rates and a 30% improvement in job satisfaction levels. Additionally, the algorithm adapts and learns from user interactions, continuously refining its recommendations based on real-time feedback.

Effects of Personalized Recommendation System Based on User Learning Behavior in English-Chinese Translation

Users may receive personalised information services and decision support from personalised recommendations. In this paper, a hybrid algorithm-based personalised recommendation approach for learning English is proposed. The user model is created by merging user interest tags, and the Person Rank algorithm is then recommended based on user information. Second, the question-and-answer model is created once the question-and-answer data has been labelled, and the Problem Rank algorithm is suggested in accordance with the question-and-answer data. Then, the approach of tag-based recommendation, comparable user recommendation, and multi-dimensional sliding window are used to construct the recommendation algorithm model. The experimental findings demonstrate that, following the model’s training with the gradient descent technique, the recommendation accuracy is steady at around 0.78, the suggested information can accommodate users who are learning English, and the personalised recommendation effect is enhanced.

Hybrid Teaching Model of College English Based on Collaborative Filtering Recommendation Algorithm

A recommendation system is a powerful tool that analyzes user preferences and behaviors to provide personalized suggestions or recommendations. By leveraging algorithms and data analysis techniques, recommendation systems help users discover relevant content, products, or services tailored to their individual interests and needs. Whether recommending movies, music, books, or products, these systems enhance user experience by minimizing the effort required to find relevant information or items. Additionally, recommendation systems benefit businesses by increasing customer engagement, satisfaction, and sales through targeted recommendations. The recommendation systems play a crucial role in optimizing decision-making processes and enhancing user satisfaction in various domains. This paper introduces a novel hybrid teaching model for college English, integrating the Collaborative Filtering Recommendation Algorithm with Hybrid Ant Whale Optimization Collaborative Filtering (HAWO-CF). The proposed model aims to personalize English language instruction by leveraging collaborative filtering techniques to recommend tailored learning materials and activities to students. Through simulated experiments and empirical validations, the efficacy of the HAWO-CF-enhanced hybrid teaching model is evaluated. Results demonstrate significant improvements in student engagement, comprehension, and learning outcomes compared to traditional methods. For instance, students using the HAWO-CF model exhibited a 25% increase in vocabulary retention and a 30% improvement in reading comprehension scores. Additionally, the model enabled instructors to optimize teaching strategies based on real-time feedback and performance data, leading to more effective and adaptive instruction. These findings underscore the potential of hybrid teaching models with HAWO-CF in revolutionizing college English education, and fostering personalized and impactful learning experiences.

A Study on Data Analytics Techniques Used By Netflix to Personalize Recommendations

This research study investigates the complex processes by which Netflix uses data analytics to personalize suggestions for each user. Knowing the nuances of recommendation algorithms becomes critical as streaming platforms continue to rule the entertainment industry. This study tries to clarify the basic ideas behind personalized content suggestions by thoroughly examining Netflix's approach, including algorithms, data collection techniques, and user engagement measures. It also explores the moral ramifications and privacy issues related to these data-driven behaviors. This study provides insights into how personalized recommendation systems will develop in the digital age by examining the effects on user satisfaction, retention, and overall business success. KEY WORDS Data analytics, recommendation algorithm, user engagement, personalized content.

Cross Category Recommendations Using LLMs

Cross-category recommendation systems aim to suggest items to users from various categories, enhancing the user experience by broadening their exposure to diverse products or services. Large Language Models (LLMs) offer a promising avenue for improving the effectiveness of such systems. LLMs, trained on vast datasets, excel at understanding complex relationships between different categories of products by analysing patterns in user interactions, preferences, and item descriptions. By leveraging natural language understanding, these models can identify similarities and connections between disparate product categories, enabling more personalized and relevant cross-category recommendations. This approach goes beyond traditional recommendation algorithms that typically focus on a single category or rely heavily on collaborative filtering techniques. Instead, LLMs can process and integrate information from multiple domains, making it easier to suggest complementary products from different categories that users may not have initially considered. Additionally, LLMs can interpret contextual information, such as user reviews and sentiment analysis, to further refine recommendations based on the user's unique preferences and behavioural patterns. The potential of LLMs in cross-category recommendations opens new possibilities for e-commerce, entertainment, and other industries where personalized experiences are key to user engagement and satisfaction. As these models continue to evolve, they offer the promise of more intelligent and comprehensive recommendation systems that can better meet the needs of users by offering diverse and relevant suggestions across categories. These abstract outlines the key role of LLMs in advancing cross-category recommendation systems, highlighting their capacity to improve personalization and broaden the scope of recommendations.

ABNS: Association-based negative sampling for collaborative filtering

The purpose of the recommendation systems (RS) is to provide personalized information filtering for users by analyzing their interactions with items. However, the sparsity of data in RS, which means that the number of negative samples is much larger than the number of positive samples, causes the necessity of the negative sampling method. Previous work has shown that an appropriate negative sampling method, rather than simple random negative sampling, can significantly improve the performance of recommendation algorithms. Unfortunately, the existing works have significant shortcomings in explainability and computational complexity. To remedy these shortcomings, we conducted a theoretical analysis of the gradient of the Bayesian Personalized Ranking (BPR) loss function and propose an explainable negative sampling method. Moreover, we conducted experiments on real-world datasets and find that the proposed negative sampling method improves the recommendation performance by an average of 10.23% over uniform negative sampling. Finally, the effectiveness of the proposed method is demonstrated through comparison with other methods and hyperparameter sensitivity analysis.

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners’ online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students’ current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner’s human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods.

Liability of ISP Under Recommendation Algorithm

Liability of ISP Under Recommendation Algorithm

Use of recommendation models to provide support to dyslexic students

Dyslexia is the most widespread specific learning disorder and significantly impair different cognitive domains. This, in turn, negatively affects dyslexic students during their learning path. Therefore, specific support must be given to these students. In addition, such a support must be highly personalized, since the problems generated by the disorder can be very different from one to another. In this work, we explored the possibility of using AI to suggest the most suitable supporting tools for dyslexic students, so as to provide a targeted help that can be of real utility. To do this, we relied on recommendation algorithms, which are a branch of machine learning, that aim to detect personal preferences and provide the most suitable suggestions. We hence implemented and trained three collaborative-filtering recommendation models, namely an item-based, a user-based and a weighted-hybrid model, and studied their performance on a large database of 1237 students’ information, collected with a self-evaluating questionnaire regarding all the most used supporting strategies and digital tools. Each recommendation model was tested with three different similarity metrics, namely Pearson correlation, Euclidean distance and Cosine similarity. The obtained results showed that a recommendation system is highly effective in suggesting the optimal help tools/strategies for everyone, with an error less then 12%. As a further evidence of the effectiveness of the implemented system, its precision was 0.85 and its recall was 0.83. The best performing filter was the hybrid one, when Pearson’s correlation is used to measure the distance among users and/or items. In addition, in a final testing performed on 50 students, dyslexic students who used the recommendation algorithm increased their academic scores of almost 1 point in a 1 to 10 scale, showing higher learning performance compared to students who did not use it. This demonstrates that the proposed approach is successful and can be used as a new and effective methodology to support students with dyslexia.

Effectiveness of Recommender Systems in Knowledge Discovery

Purpose: The general purpose of the study was to investigate the effectiveness of recommender systems in knowledge discovery.
 Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
 Findings: The findings reveal that there exists a contextual and methodological gap relating to recommender systems in knowledge discovery. The study on the effectiveness of recommender systems in knowledge discovery found that such systems played a pivotal role in facilitating users' exploration of vast information repositories, enabling them to uncover relevant resources and expand their knowledge. It found that recommender systems employing advanced algorithms and personalized techniques demonstrated higher effectiveness in generating relevant recommendations tailored to users' preferences and needs. Additionally, the study highlighted the positive correlation between user engagement metrics and knowledge discovery outcomes, emphasizing the importance of fostering active user participation in the recommendation process. Contextual information was also identified as a crucial factor influencing recommendation effectiveness. Overall, the study underscored the significance of continuous refinement and optimization of recommender system algorithms to enhance knowledge discovery outcomes for users.
 Unique Contribution to Theory, Practice and Policy: The Social Learning theory, Information Foraging theory and Cognitive Load theory may be used to anchor future studies on recommender systems in knowledge discovery. The study provided recommendations to enhance the efficacy of such systems. It suggested adopting hybrid recommender systems that combine collaborative and content-based filtering techniques to offer more accurate and diverse recommendations. Additionally, the study emphasized the importance of integrating contextual information into recommendation algorithms to dynamically adjust recommendations based on situational context. Furthermore, it recommended the use of explainable AI techniques to improve transparency and user understanding of recommendation processes. Maximizing user engagement through active participation and feedback was also highlighted as crucial, along with prioritizing recommendation diversity to foster exploration and serendipitous discovery of new knowledge resources.

Retraction Note: E-commerce User Recommendation Algorithm Based on Social Relationship Characteristics and Improved K-Means Algorithm

Retraction Note: E-commerce User Recommendation Algorithm Based on Social Relationship Characteristics and Improved K-Means Algorithm

Investigating data-driven approaches to optimize nitrogen recommendations for winter wheat

The optimal nitrogen (N) application rate is an important concept to guide the strategic/static N management of crops. However, there is still a knowledge gap on how to combine the optimal N application rate, crop growth status, meteorological, and soil multi-source information to make tactic/dynamic N recommendation. For investigating the data-driven approaches to optimize nitrogen recommendations for winter wheat, this study proposed the concept of optimal topdressing N rate that coupling ‘strategic’ with ‘tactic’ ideas. Then a direct N fertilizer recommendation algorithm based on the optimal topdressing N rate was constructed integrating multi-source data with Random Forest (RF). The agronomic, economic, and environmental benefits of the algorithm were further evaluated based on a large-scale field-level validation experiments, and compared with the indirect N recommendation algorithm of RF-based yield prediction, farmers’ conventional N application rate, and regional optimal N application rate. Finally, multi-objective evolutionary optimization was used to verify the reasonability of the N fertilizer recommendation algorithms. The results showed that the optimal topdressing N algorithm had good model performance (Random Forest prediction R2 = 0.59 ∼ 0.72, RMSE = 39.63 ∼ 40.48 kg/ha), and vegetation indices, maximum temperature, and solar radiation were important features. The validation experiment results showed that compared to conventional management measures, the optimal topdressing N algorithm could reduce N fertilizer application by 18.9 % ∼ 48.4 % with intelligent adjustment, improve partial factor productivity by 12.99 % to 46.09 %, and reduce reactive N losses and greenhouse gas emissions by 20.56 % ∼ 47.24 % and 16.34 % ∼ 40.45 %, respectively. The rationality verification of solving nitrogen topdressing rate through multi-objective evolutionary algorithm proved that both indirect and direct N recommendation algorithms could make decisions within a reasonable threshold. The study provided insights for the data-driven based N management, strategy selection, and benefit evaluation process of N recommendation.

An Effective Doctor Recommendation Algorithm for Online Healthcare Platforms

The emergence of online healthcare platforms provides patients with convenience, but choosing the right doctor among the thousands of doctors available on these platforms has become a challenge for patients. The majority of these platforms recommend the same doctors to all patients based on a global ranking, disregarding individual patient preferences. The use of recommender systems helps to resolve this issue by assisting patients in locating doctors who meet their preferences and requirements. Particularly, Collaborative Filtering (CF) algorithms have been extensively utilized to generate personalized recommendations for a variety of applications. Despite their success, they still need to be further optimized to address both the sparsity and cold-start problems due to insufficient data. In this paper, we propose an effective doctor recommendation approach to assist patients in searching for satisfactory doctors who precisely match their preferences regardless of time and location. The proposed approach employs Multi-Criteria CF and content filtering to enhance the quality of recommendations by mitigating the impact of data sparsity and cold start challenges. Offline tests conducted on a real-world dataset show that the proposed approach is superior to state-of-the-art approaches in addressing the aforementioned issues and boosting prediction accuracy and coverage.

Performance analysis of three recommendation algorithms on Amazon datasets

Recommendation systems play a crucial role in enhancing user satisfaction and driving sales for businesses. They are essential in todays marketplaces, as they are able to suggest products and services that may interest a particular individual based on their past purchases. In this paper, empirical research is conducted on three distinct subsets of the Amazon dataset, namely Sports & Outdoors, Movies & TV, and Video Games, to comprehensively evaluate the performance of three distinct recommendation methods based on deep learning algorithms. These methods include the dot product method, (an updated version of the singular value decomposition algorithm), the neural network method, and the neural collaborative filtering model with natural language processing method. The results of this study reveal that deep learning-based recommendation systems can achieve more accurate results compared to traditional recommendation systems for three types of products. The implementation of these methods on Amazons dataset can help improve sales by correctly identifying customers interests and suggesting relevant items.

Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation

Sequential Recommendation plays a significant role in daily recommendation systems, such as e-commerce platforms like Amazon and Taobao. However, even with the advent of large models, these platforms often face sparse issues in the historical browsing records of individual users due to new users joining or the introduction of new products. As a result, existing sequence recommendation algorithms may not perform well. To address this, sequence-based data augmentation methods have garnered attention. Existing sequence enhancement methods typically rely on augmenting existing data, employing techniques like cropping, masking prediction, random reordering, and random replacement of the original sequence. While these methods have shown improvements, they often overlook the exploration of the deep embedding space of the sequence. To tackle these challenges, we propose a Sparse Enhanced Network (SparseEnNet), which is a robust adversarial generation method. SparseEnNet aims to fully explore the hidden space in sequence recommendation, generating more robust enhanced items. Additionally, we adopt an adversarial generation method, allowing the model to differentiate between data augmentation categories and achieve better prediction performance for the next item in the sequence. Experiments have demonstrated that our method achieves a remarkable 4-14% improvement over existing methods when evaluated on the real-world datasets. (https://github.com/junyachen/SparseEnNet)