Product Recommendation Research Articles

Research on Personalized Recommendation of Complementary Products Based on Demand Cross-Elasticity and Hypergraphs

To improve recommendation systems, it is essential to enhance both their practicality and accuracy, thereby supporting users in making informed shopping decisions. Incorporating two types of product relationships can effectively achieve these goals: first, the product relationships, like complements, and second, the social relationships among users. However, existing studies have paid little attention to user-side information or item-side information. This paper proposes a product recommendation model that utilizes cross-elasticity of demand and hypergraphs, referred to as Hg-CR. First, users and items build a hypergraph. The user–item interactions form the hyperedges. Also, users build a hypergraph between themselves based on their social relationships. Second, hypergraph attention networks (HANs) learn the relationships between nodes. They capture the key features of nodes and hyperedges with a high degree of adaptability. A community detection algorithm organizes users into groups for product recommendations by assessing their similarities. Within different communities, individuals seek complementary products based on the cross-elasticity theory of demand. Additionally, we provide recommendations for complementary products. Tests on real datasets show that the Hg-CR model is about 10% more accurate than the other baseline models.

Mathematics of Machine Learning: An Introduction

Over the course of just a few decades, machine learning has grown into a force which shapes modern life perhaps as much as the combustion engine or wireless communication. As we drive to work, machine learning algorithms extract license plate numbers from images captured by automatic cameras at busy intersections. At work, they measure our productivity and govern our supply chains. In our personal lives, they power product recommendations on online shopping sites and suggestions on social media. In our homes and on our devices, they recognize our voices and our faces. They process our loan applications and evaluate our medical images. More globally, they stabilize power grids and assist in planning flight routes. There is not a space in our public and personal lives which machine learning has not at least begun to affect. For a field as ubiquitous, it is fairly poorly represented in our common knowledge. This article aims at giving a high level introduction to some core tasks, ideas and methods of machine learning for readers who are familiar with at least some undergraduate mathematics. Advanced readers may get more from certain sections, but our goal is to present a self-contained picture which requires little knowledge beyond calculus in multiple variables and elementary linear algebra. For readers who have not taken many of the advanced classes, this may also motivate why certain fields of study are of interest in applications. One big omission in this article are deep neural networks, i.e. the models which underly 'deep learning.' Due to their special importance, they will be discussed in a separate companion article.

Development of web based data-driven recommendation system for house rental via hierarchical fuzzy axiomatic design

PurposeIn the real estate business, identifying the ideal property for a user poses a difficult task due to the many factors involved in the decision-making process. Moreover, users often struggle to find platforms that facilitate effective communication of their preferences. To tackle this issue, a web-based data-driven recommendation system has been devised for the real estate business.Design/methodology/approachThe process of identifying the most suitable rental property for a user hinges greatly on how the user prioritizes each criterion and the analysis of unstructured data. In this research, a novel recommendation system for house rentals is developed by utilizing the Weighted Hierarchical Fuzzy Axiomatic Design (WFAD) approach. Techniques for extracting pertinent information from unstructured house descriptions are employed. The user’s preferences are captured through an interactive web application equipped with a map feature to highlight key locations.FindingsData on various available rental properties are gathered using web scraping techniques. The efficacy of the proposed rental house recommendation system is demonstrated through multiple case studies. It is observed that the developed system provides more informed and reliable decisions.Originality/valueFirst time in the related literature, we applied the weighted fuzzy axiomatic design procedure (WFAD) to the product recommendation problem and developed a comprehensive web-based system for recommending rental houses based on it in the real estate business.

HCLNet: A Hybrid CNN-LSTM Model for Enhanced Healthcare Product Classification for Recommendation

The rapid growth of healthcare commodities requires automated categorization. Effective categorization solutions save time and money and can provide precise recommendations. Poorly annotated data, inconsistency of product category, and imbalanced datasets impede machine learning algorithms in current systems. CNNs work well with large datasets but struggle with the classification of healthcare products due to class imbalance and lack of labeled data. This study presents a Hybrid CNN-LSTM (HCLNet) model to improve the classification of healthcare products and resolve these issues. This method enhances classification using CNN feature extraction and LSTM sequential pattern recognition. HCLNet can better handle class imbalance and limited labeled data with a comprehensive data preprocessing pipeline, including selection, transformation, and filtering. The hybrid design overcomes CNN constraints and captures product feature temporal connections using LSTM layers. HCLNet was compared with ResNet, GoogleNet, and AlexNet, surpassing them. HCLNet classified complex and imbalanced datasets with 96.25% accuracy, 96.60% precision, and 96.05% recall. The proposed method can improve the classification of healthcare products to obtain accurate automated product recommendations.

THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN ROAD TRAFFIC MANAGEMENT AND ITS SAFETY IMPROVEMENT

Artificial intelligence (AI) is used in many aspects of life, from personal voice assistants to product recommendations in online stores to advanced diagnostic systems in medicine. All these applications show how AI technology is becoming an increasingly integral part of everyday life. Understanding the basics of AI is key to recognizing its role in traffic management. AI can be used to monitor and control road traffic, prevent traffic jams and road crashes, support vehicle diagnostics, and optimize response times for emergency services and roadside assistance. Particular attention should be paid to reducing the number of fatal road crashes. This goal is to be achieved by integrating AI with autonomous vehicle technology, which should improve or even reduce the number of road accident victims to 0.

Dynamic long short-term memory model for enhanced product recommendations in e-commerce

Recommendation systems are pivotal for personalized user experiences, employing algorithms to predict and suggest items aligned with user preferences. Deep learning (DL) models, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), excel in capturing sequential dependencies, enhancing recommendation accuracy. However, challenges persist in session-based recommendation systems, particularly with gradient descent and class imbalances. Addressing these challenges, this work introduces dynamic LSTM (D-LSTM), a novel DL-based recommendation system tailored for dynamic E-commerce environments. The primary objective is to optimize recommendation accuracy by effectively capturing temporal dependencies within user sessions. The methodology involves the integration of D-LSTM with weight matrix optimization and a Bayesian personalized ranking (BPR) adaptable learning rate optimizer to enhance learning efficiency. Experimental results demonstrate the efficacy of D-LSTM, showing significant improvements over existing models. Specifically, comparisons with the hybrid time-centric prediction (HTCP) model reveal a performance enhancement of 19.4%, 17.2%, 35.41%, and 21.99% for hit-rate (HR) and mean reciprocal rank (MRR) in 10k and 20k recommendation sets using the Tmall dataset. These findings underscore the superior performance of D-LSTM, highlighting its potential to advance personalized recommendations in dynamic E-commerce settings.

Social Commerce: A Cost Reduction Strategic Tool for Sustaining Corporate Brands Globally. A Systematic Review Approach

Abstract: Social commerce has a strategic role in the sustainability of corporate brands. It has been discovered that little or no attention has been given to the cost-reduction strategy of social commerce to sustain corporate brands. Social commerce presents a three-way cost reduction (for manufacturers, members of trade, and consumers) to sustain brand performance globally. Social commerce works to reduce the cost of commercial advertising and other promotional elements. The study’s objective was to establish whether social commerce reduces the promotional cost of corporate brands and how that helps to sustain corporate brands. The study reviewed literature, with most spanning between 2010 and 2023, from the world and in both academia and industry. The study reveals that social commerce reduces the cost of managing corporate brands through brand trust and relationship building through social support and communities created on social media for brands. Social commerce has made many customers become company employees, resolving customer complaints and offering various forms of assistance to prospective and existing customers without adding cost to the company’s payroll. The study revealed again that customers now pay directly to disseminate brand messages shifting most of the communications cost to the customer. The cost of employing celebrities for product recommendation has been reduced due to both online and offline social commerce thus saving cost and increasing corporate brands profitability.

Social Commerce: A Cost Reduction Strategic Tool for Sustaining Corporate Brands Globally. A Systematic Review Approach

Abstract: Social commerce has a strategic role in the sustainability of corporate brands. It has been discovered that little or no attention has been given to the cost-reduction strategy of social commerce to sustain corporate brands. Social commerce presents a three-way cost reduction (for manufacturers, members of trade, and consumers) to sustain brand performance globally. Social commerce works to reduce the cost of commercial advertising and other promotional elements. The study’s objective was to establish whether social commerce reduces the promotional cost of corporate brands and how that helps to sustain corporate brands. The study reviewed literature, with most spanning between 2010 and 2023, from the world and in both academia and industry. The study reveals that social commerce reduces the cost of managing corporate brands through brand trust and relationship building through social support and communities created on social media for brands. Social commerce has made many customers become company employees, resolving customer complaints and offering various forms of assistance to prospective and existing customers without adding cost to the company’s payroll. The study revealed again that customers now pay directly to disseminate brand messages shifting most of the communications cost to the customer. The cost of employing celebrities for product recommendation has been reduced due to both online and offline social commerce thus saving cost and increasing corporate brands profitability.

Data-Driven Strategies for Enhancing Customer Retention in E-commerce

In the highly competitive e-commerce landscape, customer retention is critical for sustaining business growth and profitability. This research explores data-driven strategies for enhancing customer retention by leveraging advanced analytics, machine learning models, and behavioral data. By understanding customer preferences, purchase patterns, and engagement behaviors, businesses can implement personalized marketing campaigns, loyalty programs, and product recommendations. The study examines how predictive analytics can identify at-risk customers and proactively address their needs through targeted interventions. Additionally, it highlights the role of real-time data processing and customer feedback analysis in optimizing retention strategies. Challenges such as data privacy concerns, algorithmic bias, and the ethical use of customer data are also addressed. This research aims to provide actionable insights for e-commerce businesses, enabling them to foster long-term customer relationships, reduce churn rates, and improve lifetime value (LTV) metrics. By combining technological innovation with customer-centric practices, the study offers a comprehensive framework for creating meaningful and sustained customer engagement in the digital age.

An Exploration of Parents’ Preference for Young Children's Smart Textile Products

The topic of smart textiles has made substantial progress and garnered great interest. Nevertheless, studies are scarce regarding consumer attitudes and acceptability of these items. There is a lack of consumer studies specifically targeting the market for smart textiles designed for young children. This research seeks to close this disparity by investigating parents’ requirements and criteria when choosing smart fabrics intended for younger children. Surveys were administered to parents who had at least one child under the age of three. A nationwide sample was enlisted, yielding 267 valid responses. The results emphasized that safety is the most important factor for parents, regardless of their demographic characteristics. Parents who had previous exposure to smart textile items placed a higher emphasis on performance rather than pricing, in contrast to parents who did not have such exposure. The study additionally gathered and condensed product concepts and recommendations for subsequent product enhancement.

Word-Level Political Sentiments Inferred From Social Media and Application in Recommendation Diversification

Political polarization is commonly observed in democratic countries. While it allows individual citizens to freely choose sides, it also causes the problem of separation and isolation. Especially in information-seeking behaviors, echo chambers and filter bubbles are observed. In this paper, we present a political sentiment dictionary for analyzing political polarization and increasing information heterogeneity. It takes advantage of large-scale social media data and is thus superior in accuracy and coverage compared to manually crafted dictionaries. Generated from Japanese tweets, more than 50k words in this dictionary cover aspects ranging from political parties and public entities to foods and personal hobbies. We describe in detail the method to construct this dictionary, which can be replicated for other languages and countries. We demonstrate the use of this dictionary in the application of recommendation diversification. We show with real-world e-commerce data that the use of the dictionary can generally increase the diversity in product recommendations, effectively mitigating the filter bubbles.

Examining the persuasiveness of text and voice agents: prosody aligned with information structure increases human-likeness, perceived personalisation and brand attitude

ABSTRACT To give product and brand recommendations, marketers make use of conversational agents which increasingly communicate via voice rather than text. Existing research comparing the persuasiveness of text and voice agents showed mixed results. The quality of the speech synthesis employed may strongly influence consumers’ responses. This study investigates to what extent a voice agent with pragmatically aligned prosody is more persuasive (i.e. yields a more positive brand attitude) than an agent with a standard voice or text, and whether perceived human-likeness and perceived personalisation provide an underlying mechanism to explain these differences. In an experiment (n = 212), participants interacted with a conversational agent that recommended a camera. Results showed that a voice agent using prosody aligned to the information state of the user is more persuasive than a text agent. This effect is mediated by perceived human-likeness and perceived personalisation. Hence, aligned prosody can make synthetic speech meet a certain quality threshold to be perceived as more human-like. Theoretically, this study helps to unravel why conversational agents with human-like features are more persuasive.

Vive la résistance: a boomerang effect of resisting incentivized eWOM on consumer trust

Incentivizing consumers to share product recommendations and other forms of eWOM (electronic word of mouth) with their social media friends has become a prevalent promotional strategy among marketers. However, could this strategy potentially backfire? Adopting a metacognitive perspective, we suggest that this approach adversely affects those consumers who reject the incentive and resist sharing the message. We hypothesize that for individuals who rejected an incentive to share a message, lower initial message trustworthiness leads to decreased subsequent message trustworthiness. This effect arises as consumers reflect on their own behavior, subsequently developing perceived manipulative intent. This proposed sequential process was substantiated in two experimental studies. The results demonstrate a negative effect of initial eWOM trustworthiness on subsequent trustworthiness following incentive rejection (Studies 1-2). Furthermore, this effect is mediated by perceived manipulative intent and is bounded by the size of the incentive offered for sharing eWOM (Study 2). Both theoretical and practical implications of these findings are discussed.

Bidirectional LSTM for Electronic Product Recommendation

Bidirectional LSTM for Electronic Product Recommendation

Innovative Vegetable Chips: Harnessing the Nutritional Power of Underutilized Leafy Greens

This study investigates the acceptability of spinach-cabbage chips in three flavor variants: plain, cheese, and barbeque, specifically targeting preschoolers as the primary evaluators. A sensory evaluation was conducted, assessing the chips based on appearance, color, texture, taste, aroma, and overall acceptability. Data were analyzed using One-Way Analysis of Variance (ANOVA) to identify the most preferred nutri-chip formulation, while Kramer's Rank-Sum Test determined the favored variant among the participants. The study utilized fresh spinach and cabbage, focusing on their nutritional profiles and the production process for the chips. Key methodologies included sensory evaluations, cost analysis of ingredients, and calculations of theoretical vitamin A content, along with the percentage of the Recommended Nutrient Intake (RENI) for vitamin A per 50-gram serving for children aged 4-10 years. Results indicated that the optimal formulation comprised 200 grams of spinach and 100 grams of cabbage, with no significant differences at the 5% level in appearance and color between formulations. However, significant differences were noted in texture, taste, aroma, and overall acceptability. The spinach-cabbage nutri-chips contained 512 μg of Retinol Equivalents (RE) of vitamin A, translating to 12.8% of the daily recommended intake for preschoolers. The findings suggest that spinach-cabbage nutri-chips can serve as a nutritious snack option to enhance vitamin A intake among preschoolers and may be beneficial for supplementary feeding programs. Recommendations for production, packaging, and further research into the product's shelf life and applicability in school feeding initiatives are provided.

Revolutionising Skincare Through Airtificial Intelligence

Abstract: Background: Over the last 10years there has been a drastic shift in the development of computer-based systems, artificial intelligence, algorithms and technology in the world of dermatology be it cosmetic or even retail production of products sold to customers with high-tech technology and innovative means to build their client base through customer delight. Artificial Intelligence is particularly helpful in recognizing the type of skin customers have and suggesting solutions which help customers pick from the range of options given to them. Most e-commerce platforms have begun using such AI driven soft wear to help customers while they are in the comfort of their homes to choose from a range of cosmetic options right from lipsticks, concealers, foundation, eyeshadows, shades of tins, serums etc. Given that every person has a different skin type, these applications ensure 100 percent personalized suggestions. This new trend is sending shock waves across the retail sector around the world attracting a massive customer base to adapting to AI Derma logy and retail. The intelligent software used by sever companies not only benefits the customer, but also helps companies with their operational efficiency as they can use AI algorithms to predict and forecast the inventory to help reduce over production and excessive stock production minimizing product wastage and improving sustainably.AI is booming in both clinical diagnosis as well as giant companies such as Loreal and Olay who are dedicated to providing customers with advanced tools like skin analyzers or apps based on predefined rules which help AI-powered tools analyze skin conditions, age, and personal skincare history to provide highly personalized product recommendations. In clinical diagnosis AI offers valuable insight to the patient’s skin and condition be it cancer or other skin diseases making judgments to assist the doctors and dermatologist in making informed decisions regarding the line of treatment for the patient in question. This includes screening, diagnosis, treatments and predictions of the treatment outcomes. These new innovative and robust AI systems leaving customers feeling confident and have not begun relying on AI driven treatments and procedures

Application of K-Nearest Neighbor Algorithm for Consumer Behaviour Identification and Product Personalisation Based on Big Data Analysis

The rapid digital transformation has fundamentally altered the retail industry, presenting challenges such as shifting consumer behavior and intensified market competition. This research explores the application of the K-Nearest Neighbor (KNN) algorithm for identifying consumer behavior and developing product personalization systems based on big data insights. Utilizing a dataset comprising 10,000 transaction records from January to December 2023 and 5,302 product types, we implemented the KNN algorithm to predict consumer purchases. The data was processed into 89,908 distinct transaction records. Our evaluation, using 5-fold cross-validation, demonstrated that the optimal performance of the KNN model was achieved with 𝑘=10, yielding a precision of 0.8319, recall of 0.8311, and an F1-score of 0.8312. These findings highlight the effectiveness of KNN in enhancing consumer satisfaction through precise product recommendations. This study provides strategic insights for modern retailers aiming to leverage AI and big data to remain competitive and meet evolving consumer expectations.

The Role of Machine Learning in Enhancing Corporate Financial Planning

Aim: To examine the role of machine learning in enhancing corporate financial planning. Problem Statement: In the past, corporate financial planning relied on statistical models and expert judgment which are often constrained by predefined assumptions and historical data. By so doing, they usually struggled to handle the non-linear and dynamic nature of financial markets causing limitations in their responsiveness and predictive power. Significance of Study: There is need to incorporate machine learning in corporate financial planning to provide a transformative technique via the introduction of advanced algorithms which are capable of analyzing diverse and vast datasets to uncover sophisticated relationships and patterns. Methodology: Recent relevant published articles in the area of machine learning in enhancing corporate financial planning were consulted. Relevant articles were sourced from the internet using the Google search engine. The abstract of the consulted published articles were thoroughly examined to study their relevance to the subject matter. Discussion: This review article examines the role of machine learning in enhancing corporate financial planning. It was found that machine learning techniques have crucial roles to play in the context of corporate financial planning via decision-making improvement, risk management, operational efficiency enhancement, fraud detection and enabling personalized experiences. Also, Machine Learning technologies have a wide variety of uses in corporate financial planning which enable banks and insurance companies in delivering tailored experiences that give their customers some preferences. Common identified machine learning approaches in corporate financial planning include predictive analytics, natural language processing, computer vision, reinforcement learning and anomaly detection approaches. Major areas of machine learning application include dynamic pricing and offers; regulatory compliance; personalized customer service; predictive analytics for customer retention; personalized product recommendations and so on. Furthermore, the implementation of Machine Learning (ML) in corporate financial planning and financial services industry provides many chances for enhancing customer experiences, improving efficiency and driving innovation. However, ML poses numerous challenges which should be addressed to attain the complete potential of these technologies. Conclusion: As technology continues to advance, financial institutions will need to route the challenges of bias mitigation, transparency and regulatory compliance while the ML full potential is equally leveraged. Also, machine learning has the potential to transform corporate financial planning via the provision of more adaptive, accurate and real-time insights.

Regulating robo-advice for consumers’ financial decisions: The interplay between algorithm quality & digital choice architecture

Rapid advances in digital interfaces, the quality of predictive algorithms, and the availability of high-speed internet are allowing firms to offer robo-advice: automated online guidance about which financial products are suitable for a consumer. At their best, robo-advisors feature algorithms that accurately predict which financial products are the optimal match for a given consumer’s needs and use digital choice architectures (that is, ways of presenting the product recommendations online) that are designed to strongly guide consumers to those best-fit products. Yet when algorithms lack great accuracy, strong guidance in favor of products incorrectly predicted to have the best fit could lead consumers to select options that do not meet their needs well. The possibility that strong guidance results in different financial outcomes depending on the quality of the algorithms has important implications for regulating robo-advice. We propose that, consistent with current regulatory practice for human advisors, regulators should require any firm wishing to implement a robo-advisor for its clients to demonstrate both that the robo-advisor is honest (that is, it works in the best interests of the client) and that its algorithms meet some established level of accuracy. In addition, regulators should require any firm offering a robo-advisor to demonstrate that the strength of the guidance provided by the digital choice architecture aligns with the predictive accuracy of the advisor’s algorithms. We offer several practical suggestions for implementing such a regulatory strategy. The need for regulations is becoming increasingly urgent because many robo-advisors now use artificial intelligence (AI) to make predictions. AI has made robo-advisors more powerful and easier to use, which is likely to expand their adoption—and, by extension, the harm that would be caused if subpar robo-advisors are allowed on the market.

RESPONSE OF LIQUID MANURES AS ALTERNATIVE NUTRIENT ON GROWTH AND YIELD OF TOMATO

This research explores the transformative role of advanced technologies, particularly AI, machine learning (ML) and block chain, in the development of smart e-commerce solutions that enhance user experiences and operational efficiencies. It also explores how customized algorithms can improve customer engagement, boost conversion rates and encourage sustainable shopping practices. Additionally, the research investigates AI-driven solutions to encourage sustainable shopping behaviors and optimize shopping post-purchase experiences. Specifically, it aims to assess the impact of AI-based personalization algorithms, examine the trade-offs between data privacy and user experience and analyze AI's influence on consumer trust and decision-making processes. Primary data from surveys and interviews are supplemented with secondary data on customer behavior and sales metrics to evaluate the effectiveness of technologies like AI, machine learning and block chain in driving personalized, ethical and efficient e-commerce. A mixed-method research design, combining qualitative and quantitative approaches, employed to capture insights from key stakeholders (e.g., consumers, developers and e-commerce managers) and assess the impact of smart e-commerce platforms on shopping behavior, trust and loyalty. Implementing the Agile Model, the research iteratively addresses system requirements, testing and deployment challenges to create a flexible, scalable solution. Key findings underscore the role of predictive modeling in inventory management, the ethical potential of sustainable product recommendations and the positive consumer responses to transparency initiatives. The study contributes valuable insights into the development of secure, personalized and ethical e-commerce platforms that align with modern consumer expectations for convenience and responsibility.