Recommender Systems Research Articles

Design and Implementation of a Personalized Text-Based Recommendation System

Abstract: We all know that in the competitive & ever evolving Age of content -less is more and relevancy increases engagementwhich should be focal on UX. In this paper, we describe the designing and implementation of a text-based recommendation system that specializes in offering automatic personalized recommendations for textual content-dependent on user preferences, and behavior. Analyze text data and receive tailored, contextual recommendations using Natural Language Processing (NLP) and machine learning methods. This system combines collaborative, content-based and hybrid filtering techniques along with adaptive learning to improve the accuracy of recommendations over time as user interests change. Analysis of real-world datasets indicates that the system dramatically enhances recommendation precision and user satisfaction. The results highlight the promise of tailored recommendations in areas like commerce and digital media to online education, primarily filtering great volumes of information for a more customized experience

Extracting Political Interest Model from Interaction Data Based on Novel Word-level Bias Assignment

In democratic countries, political interest is deeply involved in people's daily lives. Research in political consumerism shows that product purchase decision is also influenced by the political orientation of the consumer. In traditional recommendation system design, user interest in an item is provided by a unified model. Recently, interest disentanglement methods have been introduced. It is shown that by disentangling interest factors such as conformity and private interest, recommendation performance can be significantly improved. However, few studies attempt to disentangle political interest in purchase behavior, which is bipolar. In this paper, we propose a method to extract political interest model from e-commerce interaction data, which is supported by a novel word-level political bias assignment. For the bias assignment part, we improved a political bias distilling method. For the political interest model extraction part, we extend a one-side bias method to make it support bipolar bias. We compare our method with state-of-the-art baseline methods in several evaluation settings, and the experimental results show that our method can achieve superior performance. Further investigation shows that our method is consistent with theories of political consumerism.

Improvement of E-commerce Recommender System Using Hybridization of Bert, Matrix Factorization and Attention Mechanism

Improvement of E-commerce Recommender System Using Hybridization of Bert, Matrix Factorization and Attention Mechanism

Fair and Efficient Ridesharing: A Dynamic Programming-based Relocation Approach

Recommending routes by their probability of having a rider has long been the goal of conventional route recommendation systems. While this maximizes the platform-specific criteria of efficiency, it results in sub-optimal outcomes with the disparity among the income of drivers who work for similar time frames. Pioneer studies on fairness in ridesharing platforms have focused on algorithms that match drivers and riders. However, these studies do not consider the time schedules of different riders sharing a ride in the ridesharing mode. To overcome this shortcoming, we present the first route recommendation system for ridesharing networks that explicitly considers fairness as an evaluation criterion. In particular, we design a routing mechanism that reduces the inequality among drivers and provides them with routes that have a similar probability of finding riders over a period of time. However, while optimizing fairness the efficiency of the platform should not be affected as both of these goals are important for the long-term sustainability of the system. In order to jointly optimize fairness and efficiency we consider repositioning drivers with low income to the areas that have a higher probability of finding riders in future. While applying driver repositioning, we design a future-aware policy and allocate the areas to the drivers considering the destination of requests in the corresponding area. Extensive simulations on real-world datasets of Washington DC and New York demonstrate superior performance by our proposed system in comparison to the existing baselines.

IBCFaiCDR: Auxiliary data-driven item-based collaborative filtering in cross-domain RSs to address user cold start problem

The adoption of Recommender Systems (RSs) in various services is imperative in the present era. One of the inherent challenge faced by RSs is the user cold start problem (UCS) and it occurs when there is a lack of enough data available for a new user, which restricts the capacity to provide accurate recommendations. Researchers have conducted investigations into the utilization of auxiliary data like social network data, review text, demographic information etc., to augment the RSs ability to learn and understand the user's preferences, with the aim of solving this issue. The provided information is inadequate due to the restricted level of user engagement within the specified domain. The researchers utilize a Cross-Domain RS (CDR) that incorporates information from multiple domains to provide personalized suggestions to users. Item Based Collaborative Filtering (IBCF), which utilizes item-item similarity, is commonly used when dealing with sparse data. The suggested collaborative filtering technique employs IBCF which uses the item similarity obtained with item features. These features are generated by the proposed approach IBCFaiCDR's model, trained on source domain data such as product images and reviews. The efficacy of the proposed approach is assessed using a large dataset from Amazon, consisting of user interactions across many categories. The results on target domain, evaluated using MAE and RMSE, indicate that the inclusion of auxiliary information (image + text) enhances the RS performance for new users.

The Impact of Artificial Intelligence on Marketing Strategies

The research project examines the impact of Artificial Intelligence (AI) on marketing strategies across various industries, focusing on how AI-driven technologies enhance customer engagement and optimize marketing campaigns. The objective of this study is to explore the diverse effects of AI on marketing practices and to identify the challenges and ethical considerations that arise from its implementation. The research was conducted through a qualitative methodology that conducted interviews with 18 marketing professionals to gather information about their experiences and perceptions regarding AI in marketing. The findings reveal a significant trend toward the adoption of AI and machine learning technologies. Participants noted their effectiveness in personalizing customer interactions and improving data-driven decision making. Key themes include the success of AI-driven tools such as recommendation systems and predictive analytics in enhancing marketing effectiveness. The researchers highlighted some ethical concerns which included data privacy, algorithmic bias, and the need for transparency. The research study validates the importance of data literacy and AI competence among marketing professionals. It suggests that organizations prioritize skill development to adapt to the evolving marketing landscape. The research contributes to the existing body of knowledge by emphasizing the dual role of AI as a transformative force in marketing while also raising critical ethical issues. The research offers practical recommendations for businesses to effectively utilize Artificial Intelligence while ensuring they remain competitive in the global marketing landscape.

Attribute-Aware Graph Convolutional Network Recommendation Method

In recent years, recommendation systems have made significant strides through the application of graph neural networks (GNNs). However, most of the existing methods primarily focus on modeling user–item interactions, often failing to account for the distinct roles of different types of neighboring nodes during the graph convolution process. Moreover, the intricate relationships between user and item attributes are frequently underexplored, which constrains further improvement in the performance of recommendation models. To overcome these challenges, this paper proposes an attribute-aware graph convolutional network recommendation model (AAGCNR). This model accounts for the complex interrelationships between user and item attributes while distinguishing between different types of neighboring nodes during graph convolution. First, a multi-head self-attention mechanism is applied to capture the semantic relationships among attributes across various semantic spaces. Additionally, a bilinear interaction module is employed to facilitate interactions between attributes. Since different neighboring nodes exert different influences on target nodes, the model performs convolutional aggregation by leveraging the interrelationships of these attributes throughout the graph convolution process. Experiments conducted on real-world datasets reveal that AAGCNR outperforms other benchmark algorithms, particularly in terms of RECALL and NDCG metrics.

IMPLEMENTASI METODE NAÏVE BAYES UNTUK PREDIKSI KELAYAKAN BANTUAN MODAL DAN KREDIT USAHA DI DESA SELAT ABIANSEMAL BADUNG

Small, Micro, and Medium Enterprises (MSMEs) are small-scale businesses carried out by individuals or business entities with a certain amount of net worth and sales proceeds. MSMEs in Abiansemal Strait Village, Badung, still use manual methods in analyzing data. Conditions like this are the basis for this study to create a system that can help make it easier for the Abiansemal Badung Strait Village if there is an assistance program from the government, both business capital assistance, and business credit applications to be given to MSME actors. Naïve Bayes' method of classifying MSMEs is built on concepts explored from the interview process, literature studies and system implementation. The design of this system was made using the Python programming language. The CRISP-DM model is a system development method used in the classification of MSMEs. The Feasibility Prediction Model for Capital Assistance and Business Credit using the Naïve Bayes Method can make it easier for users to find out whether the existence of a web-based classification and recommendation system can make it easier for users to carry out the MSME classification process easily and users can see the solutions recommended by referral MSMEs that have better business conditions directly.

Review-Based Explainable Recommendations: A Transparency Perspective

With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, many of them remain difficult to diagnose what aspects of the deep models’ input drive the final ranking decision, thus, they cannot often be understood by human stakeholders. In this paper, we investigate the dilemma between recommendation and explainability, and show that by utilizing the contextual features (e.g., item reviews from users), we can design a series of explainable recommender systems without sacrificing their performance. In particular, we propose three types of explainable recommendation strategies with gradual changes in model transparency: whitebox, graybox, and blackbox. Each strategy explains its ranking decisions via different mechanisms: attention weights, adversarial perturbations, and counterfactual perturbations. We apply these explainable models on five real-world data sets under the contextualized setting where the users provided explicit reviews of the items. The empirical results show that our models can achieve highly competitive ranking performance, and meanwhile, generate accurate and effective explanations in terms of numerous quantitative metrics and qualitative visualizations.

AI-driven personalization framework for SMES: Revolutionizing customer engagement and retention

In today's competitive business landscape, Small and Medium Enterprises (SMEs) face unique challenges in building and maintaining strong customer relationships. An AI-driven personalization framework offers a transformative solution by enabling SMEs to deliver highly targeted and individualized customer experiences, improving both engagement and retention rates. This review outlines how artificial intelligence (AI) can empower SMEs by integrating data-driven insights with customer interaction processes to revolutionize business practices. AI-driven personalization leverages machine learning algorithms, natural language processing (NLP), and predictive analytics to analyze customer behaviors, preferences, and feedback. By aggregating data from various sources—such as online interactions, purchase histories, and social media activity—AI can generate personalized recommendations, offers, and communication strategies that resonate with individual customers. The framework facilitates dynamic customer segmentation, allowing SMEs to tailor marketing efforts and enhance service delivery. The personalization process also extends beyond marketing by optimizing customer support through AI-powered chatbots and recommendation systems, which provide real-time solutions and advice. This level of personalization fosters stronger emotional connections, increasing customer satisfaction and brand loyalty. Additionally, AI-driven insights enable SMEs to anticipate customer needs, predict churn rates, and proactively address potential issues, thereby boosting retention rates. For SMEs, the implementation of an AI-driven personalization framework is not only cost-effective but scalable, making it accessible even to businesses with limited resources. As SMEs increasingly adopt digital tools, the integration of AI-based personalization becomes essential for staying competitive in a rapidly evolving market. This review highlights the potential of AI in transforming customer engagement strategies for SMEs by offering a tailored, efficient, and sustainable approach to enhancing customer experiences.

Design of adaptive recommendation system for autism children using optimal feature selection-based adaptive dilated 1DCNN-LSTM with attention mechanism

One kind of neurological disorder is caused in the brain which is defined as Autism Spectrum Disorder (ASD). It has acquired the symptoms that appear in young children. In addition to that, it influences how the individual behaves and learns as well as communicates and interacts with others. More specifically, the term Autism is defined as a developmental disorder that impacts communication and social skills and it may vary from mental handicap cases to relieving superior cognitive abilities, intact, and the characteristic pattern of poor. Moreover, the school activities have acquired various difficulties to the given model that include changes in expected routines, intense sensory stimulation, noisy or disordered environments, and social interactions. Consequently, the conventional approaches face certain limitations like user privacy, scalability, and cold-start. Here, a novel suggestion system for autistic children is developed to detect distractions and anxious situations using deep learning and then treat the children based on their abilities. It has helped to prevent the risk to children. The data is given to the selection of the feature stage. The weight optimization is performed using the Modified Garter Snake Optimization Algorithm (MGSOA) during the selection of features. Then, the selected features are given to the Adaptive Dilated One Dimensional Conventional Neural Network (1DCNN) and Long Short-Term Memory (LSTM) with Attention Mechanism termed AD-1DCNN + LSTM-AMfor detecting the autism disorder for children. Here, the parameter optimization is performed using MGSOA optimization. It effectively forecasts the symptoms in a short time. This optimization helps to provide reliable and flexible outcomes for the developed recommendation system for autistic children. The developed recommendation system for autistic children is compared to baseline techniques with efficacy metrics to visualize elevated results.

Node Importance Estimation for Knowledge Graphs Based on Multi-Perspectives Attention Fusion Mechanism

The advent of the Internet has led to the emergence of multi-source heterogeneous knowledge graphs, which have become a crucial means of storing and disseminating knowledge. Node importance estimation is a technique that is employed extensively in a number of fields, including recommender systems, intelligent search and resource allocation. This study introduces a Multi-perspective attention mechanism Fusion Algorithm for the mapping of multi-perspective features of knowledge graphs to Node Importance Estimation (MFA-NIE). First, structural embedding features, relational predicate features, and attribute features (both textual and quantitative) are established for the nodes. Subsequently, an enhanced attention mechanism is employed to extract, compress, and fuse these features. The fused hidden layer vector is employed in the design of a key-based attention mechanism, which enables the propagation of messages from neighboring nodes to the source node. This process results in the iterative updating of the source node’s hidden features. Finally, TOPSIS centrality, based on the topology of the source node, is employed to dynamically adjust the mapping between the fused features and node importance. Experiments were conducted on real-world, large-scale, multi-source, heterogeneous knowledge graphs, comparing the MFA-NIE algorithm with traditional and advanced baseline algorithms, including PR, PPR, GENI, RGTN, CLINE, MCRL, and others. The results demonstrate that the MFA-NIE algorithm significantly improves effectiveness and accuracy, showcasing its superiority, versatility, and practical application value.

Designing an Effective Job Recommender System based on Embedded Machine Learning Models

The need to automate employment offers to qualify job searchers has gain attentions. For an automated recommendation systems to be used more frequently, a better user-friendly filtering techniques are required. This paper designs an automated process, referred to as “the job recommender”, which focuses on user-centric design and personalization for recommending and matching applicants with appropriate jobs. We use the bottom-up approach that uses dataset based on filtering algorithms to predict and make recommendations for job seekers. The algorithm helps the recruiters to produce the list of the résumé that best meets the job descriptions. In this context, the random forest (RF) and support vector machines (SVM) are adopted to train the data. They are supplied personalized information (qualifications, result of aptitude test, age, and work experience) reported on the résumés of individual candidates from the pool of submissions, and the system train data to learn the evolution of job selection by candidates based on these machine learning tools. The algorithm used would help the recruiters to produce the list of the résumé that best meets the job descriptions. The algorithms are designed to recommend personalized items tailored to each user's interests. Under the minimum hardware and software requirements, the job recommender system was implemented in streamlit - a python template, for designing the frontend.

Exploring the Efficiency of Hybrid Recommender Systems Implemented with TensorFlow Framework

In recent years, the field of recommendation systems has seen significant advancement with the introduction of hybrid approaches. These systems combine the strengths of multiple recommendation techniques to provide more accurate and diverse recommendations to users. In this research, we propose and evaluate the effectiveness of a hybrid recommender system that utilizes TensorFlow, an open-source machine learning framework, to implement the system. The proposed system combines both collaborative and content-based methods to remove the cold start problem and make personalized recommendations that can recommend similar movies to the users based on features extracted by the model. The results of our experiments demonstrate that the proposed hybrid system outperforms traditional singular methods and can be effectively implemented using TensorFlow. This research provides insights into the potential of TensorFlow for building efficient hybrid recommendation systems and the benefits of combining multiple recommendation techniques.

The Advantages of Health Recommending System with Wearable Devices

The health recommendation system is one of the critical application scenarios of machine learning technology. With the advantages of wearable and mobile devices to collect rich user health monitoring data, machine learning algorithms can accurately advise users’ health status and corresponding living habits. However, some technical and regulatory challenges still need to be solved at this stage. Specifically, to ensure real-time recommendation results, the health recommendation system needs to adopt the method of training computational learning algorithms on wearable devices. However, due to wearable devices’ limited computing resources and storage space, it is often difficult to obtain satisfactory results by changing the strategy. In addition, the issue of data privacy deserves attention because wearable devices collect a lot of user privacy information, but the data manager is often not the user himself. Finally, Bluetooth protocol usually connects wearable devices, making them vulnerable to intentional attacks. This paper investigates the above problems and expounds on the solutions to related problems.

Text Comment Classification and Sentiment Analysis Model Based on LSTM

This study addresses the challenge of sentiment analysis and text classification in user-generated comments, an essential task for understanding public opinion in the digital age. With the widespread adoption of social media, the sheer volume of text data has increased exponentially, thereby necessitating advanced analytical tools. The study integrates Long Short-Term Memory (LSTM) networks with attention mechanisms to develop a model adept at both classifying and analyzing the sentiment of textual comments. The research methodology commences with data cleaning, tokenization, and feature extraction, subsequently followed by the implementation of a bidirectional LSTM architecture augmented with an attention mechanism. The model underwent rigorous training and testing on a comprehensive dataset, yielding high accuracy rates in sentiment analysis and text classification. The results underscore the model’s proficiency in capturing sentiment nuances and underscore its potential for deployment in e-commerce and content recommendation systems, emphasizing the practical implications of this research for enhancing decision-making processes in businesses and among policymakers.

EvalDepApps: How to evaluate depression management based on evidence and end users

Abstract The use of apps is increasing in the field of mental health due to their ease of use and accessibility, although there is not enough evidence on their effectiveness and safety. EvalDepApps aims to develop an evaluation tool for depression management apps. A systematic review with meta-analysis (SRMA) was performed to evaluate the efficacy and safety of apps for depression, and to identify the evaluation criteria used. PRISMA methodology was followed. The MEDLINE, PsycINFO, and Embase databases were consulted. The risk of bias was assessed with the RoB2 scale. An online 2 rounds Delphi was carried out to prioritize the most relevant criteria identified. 44 people (26 professionals, 18 patients) were invited. They were asked to rate the importance of each criterion on a Likert scale (1 - 6). Those that obtained a high consensus were selected; those with a medium were submitted to the 2nd round. Empathization (6) and co-design (6) sessions were held with patients (23) and professionals (33) in Catalonia, the Canary Islands and Andalusia to identify what relevant aspects the tool should have. Twenty-nine studies were included in the SRMA (67% unclear bias), finding a significant effect of mHealth interventions in reducing depressive symptoms compared to non-active control (Hedges g = −0.62, 95% CI: −0.87 to − 0.37, I2 = 87%). In Round 1 of the Delphi (59% participation) 24 criteria obtained a high consensus, 20 a medium and 7 a low. In Round 2 (52% participation), 4 criteria reached high consensus. The empathy sessions showed that the actions most requested by patients were reduce anxiety, and information about their condition; for professionals, suicide prevention. Regarding co-design, it was proposed that the tool provide ranking of the apps, recommendation systems and a very visual format. The RSMA and Delphi guarantee that the tool will be based on scientific evidence and expert judgment, while empathization and co-design that it will fit with the needs of end users. Key messages • It is relevant to evaluate the quality of health apps, especially those addressed to vulnerable populations such as people with depression. • It is crucial that the development of evaluation tools for digital health interventions will be developed based on evidence-based but also includes end users.

Future Trends of Open-Source AI in Libraries: Implications for Librarianship and Service Delivery

This paper explores the future trends and implications of open-source artificial intelligence (AI) for libraries, focusing on predicted technological advancements, long-term impacts on library operations, and the evolving role of librarians. Key advancements, such as enhanced natural language processing, intelligent recommendation systems, and advanced data analytics, are expected to significantly improve user experience and operational efficiency. The implications of these technologies include more personalized and responsive service delivery, streamlined operations, and an evolution in the roles and responsibilities of library staff. Librarians will need to develop new skills and advocate for ethical AI use, ensuring that AI applications align with the library’s values of inclusivity and accessibility. Additionally, the paper discusses the challenges of adopting open-source AI, including technological complexity, resource constraints, and data privacy concerns. The paper concludes that embracing open-source AI fosters innovation and collaboration, positioning libraries as vital hubs of knowledge and community engagement in the future.

A Study of Recommendation Methods Based on Graph Hybrid Neural Networks and Deep Crossing

In the face of complex user behavior patterns and massive data, improving the performance of recommender system models is an urgent challenge. Traditional methods often struggle to effectively handle feature interactions and complex user-item relationships. Combining the advantages of graph neural networks and the Deep Crossing network, this paper proposes a recommendation method based on hybrid neural networks with Deep Crossing (Deep Crossing with Graph Convolution and GRU, DCGCN-GRU). First, by constructing the graph structure of users and items, higher-order feature representations are extracted, and node features are updated using a multilayer graph convolution operation. Then, the higher-order features learned by the graph convolution network are spliced and weighted with the original features to form new feature inputs. Next, a Gated Recurrent Unit (GRU) is introduced to capture the inter-feature temporal dynamic relationships and sequence information. Finally, the Deep Crossing model is utilized to learn the interactions between the fused features at multiple levels and enhance the interactions between the features. Comparative experiments on three public datasets, MovieLens-ml-25m, Book-Crossings, and Amazon Reviews’23, show that the model achieves significant improvements in accuracy, mean square error (MSE), and mean absolute error (MAE).

Retraction Note: Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques

Retraction Note: Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques