Personalized Product Recommendations Research Articles

Data to Decisions: Optimizing E-commerce Sales Potential with Analytics

Descriptive, diagnostic, predictive, and prescriptive analytics play crucial roles in optimizing the performance and user experience of an e-commerce site. Descriptive analytics involves examining historical data to gain insights into past performance, enabling businesses to identify trends, patterns, and anomalies. This analysis helps in understanding what has happened, such as identifying popular products or peak sales periods, and provides a foundation for further analysis. Diagnostic analytics goes beyond descriptive analytics by examining why certain events occurred, identifying factors that influenced outcomes, and uncovering strengths, weaknesses, and areas for improvement within an e-commerce platform. Predictive analytics utilizes statistical algorithms and machine learning techniques to forecast future trends and outcomes, enabling businesses to anticipate customer preferences, demand for specific products, and potential sales opportunities. By leveraging predictive insights, e-commerce sites can adjust strategies, inventory levels, and marketing campaigns proactively to stay ahead of the competition and meet evolving customer needs. Prescriptive analytics takes predictive insights to the next level by recommending specific actions or strategies to optimize business processes and achieve desired outcomes. This could involve personalized product recommendations, targeted marketing strategies, or dynamic pricing adjustments based on real-time data analysis. By harnessing these four types of analytics, e-commerce businesses can make informed, data-driven decisions, enhance customer experiences, drive sales growth, and maximize profitability.

Using Web3 technologies to represent personalized consumer taste preferences in whiskies

The emergence of Web3 technologies in the recent decade offers innovative tools and protocols that can revolutionize market research, which faces the ongoing challenges of comprehending consumer needs. One possibility is to securely store and represent personalized consumer preferences. This manuscript illustrates the application of Non-Fungible Tokens (NFTs) as a means to present consumer preference data and deliver personalized product recommendations directly to consumers. To do this, we initiated a study involving a small group of 31 respondents who participated in the “What’s Your Whisky” survey. Their responses were transformed into unique pieces of artwork generated by an algorithm. Furthermore, whisky recommendations were derived from these survey responses and integrated into the corresponding artwork. These personalized artworks, which represent each respondent’s whisky taste preference and top whisky selection, can be minted onto a blockchain as NFTs. This process enables respondents to securely own and retain them in their digital wallets, thus creating an immutable record of their whisky taste preferences and personalized recommendations. Finally, a follow-up survey was conducted to assess the value of personalized artwork NFTs. Respondents were asked for preferences between artwork derived from their taste preferences to those with the same whisky recommendations but with randomly sourced background art. Binomial test results revealed a clear preference for artwork based on their personal affirmed that respondents favored artwork based on their personal survey responses.

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

Dynamic Product Recommender systems play a crucial role in e-commerce, guiding users towards products that align with their preferences and enhancing the shopping experience. However, existing systems often struggle to capture the nuances of user preferences, particularly when dealing with multiple features via keyword queries and filters. Additionally, scalability challenges arise as the volume of products grows with features and values. In addition, the static filters of existing recommender systems limit the exploration by strictly adhering to user selections, hindering discovery of potentially better features within the preferred budget or category. This can lead to user dissatisfaction and missed opportunities to discover more desirable options. This research addresses these limitations by introducing FacetDRS, a novel dynamic product recommendation system that leverages flexible feature matching and a scalable architecture. FacetDRS empowers the recommendations with three distinct matching methods to cater to diverse preference nuances. Exact Match ensures precise adherence to user specifications, while High-Impact Partial Match prioritizes core features with flexibility in non-critical aspects. Extended Partial Match suggests relevant options beyond initial preferences, leveraging predefined thresholds. This multi-faceted approach captures the full spectrum of user desires while maintaining accuracy. To evaluate the effectiveness of FacetDRS, a rigorous experimental setup was conducted, comparing its performance across Keyword Match Score, First Selections in Top-K and Average Response Time metrics with established recommender systems such as DeepRec, APGNN, and TADCF. The results demonstrate that FacetDRS achieves superior performance than its counterparts in all comparison aspects. The system's flexible matching methods, combined with its robust architecture, offer a valuable solution for enhancing user satisfaction and driving conversions in the mobile commerce landscape.

The Role of Machine Learning in Enhancing the Customer Relationship of Indian E-commerce Sector

The integration of machine learning technology in the Indian e-commerce sector shows promise in bolstering customer relationships. By leveraging machine learning within customer relationship management (CRM) systems, businesses can analyze customer behaviors and make data-driven decisions which facilitates personalized advertisement campaigns, improving customer engagement and satisfaction. Machine learning aids in developing customer churn systems to identify and address factors contributing to customer attrition, enhancing overall satisfaction levels. The technology's data mining capabilities enable the prediction of customer purchasing behavior, allowing for personalized product recommendations and improved brand awareness. Furthermore, machine learning models, such as the random forest algorithm, assist in analyzing customer characteristics to devise effective relationship enhancement strategies. Moreover, sentiment analysis and natural language processing functionalities of machine learning contribute to automating customer service, further strengthening customer relationships within the Indian e-commerce landscape.

AI IN PROJECT MANAGEMENT: EXPLORING THEORETICAL MODELS FOR DECISION-MAKING AND RISK MANAGEMENT

This paper explores the transformative potential of Artificial Intelligence (AI) in personalized marketing. It highlights how AI can analyze vast amounts of customer data to create targeted messages, recommendations, and real-time interactions that resonate with individual needs and preferences. This personalized approach fosters deeper consumer engagement, leading to increased satisfaction, brand loyalty, and business success. The paper discusses the future potential of AI in shaping personalized marketing experiences. However, responsible implementation will be paramount in ensuring a positive future for both brands and consumers. Enhanced version of the abstract incorporating additional insights, this paper delves into the transformative power of Artificial Intelligence (AI) in personalized marketing. It explores how AI algorithms can analyze a multitude of customer data points, including purchase history, website behavior, and social media interactions. This rich data empowers brands to create highly targeted messages, recommendations, and real-time interactions that resonate with individual customer needs and preferences. By fostering deeper consumer engagement, AI-powered personalization unlocks a pathway to increased customer satisfaction, brand loyalty, and ultimately, significant business growth. However, the paper acknowledges the ethical considerations that accompany AI implementation. Responsible data practices are paramount, ensuring data security and mitigating bias in AI algorithms to prevent discriminatory marketing practices. Transparency in how data is collected and used builds trust with consumers, fostering a mutually beneficial relationship. Looking ahead, the paper explores the vast future potential of AI in personalized marketing. Imagine AI-powered Chat bot offering personalized product recommendations in real-time, or virtual reality experiences tailored to individual preferences. The future of marketing lies in creating genuine connections with consumers, and AI provides the tools to personalize the customer journey at every touch point. However, navigating the ethical landscape and prioritizing responsible data practices will be crucial in ensuring a positive future for both brands and consumers.
 Keywords: Artificial Intelligence (AI), Personalized Marketing, Customer Engagement, Customer Data, Marketing Strategy.

A Novel Deep Learning-Based Visual Search Engine in Digital Marketing for Tourism E-Commerce Platforms

Visual search technology, because of its convenience and high efficiency, is widely used by major tourism e-commerce platforms in product search functions. This study introduces an innovative visual search engine model, namely CLIP-ItP, aiming to thoroughly explore the application potential of visual search in tourism e-commerce. The model is an extension of the CLIP (contrastive language-image pre-training) framework and is developed through three pivotal stages. Firstly, by training an image feature extractor and a linear model, the visual search engine labels images, establishing an experimental visual search engine. Secondly, CLIP-ItP jointly trains multiple text and image encoders, facilitating the integration of multimodal data, including product image labels, categories, names, and attributes. Finally, leveraging user-uploaded images and jointly selected product attributes, CLIP-ItP provides personalized top-k product recommendations.

The Study of Artificial Marketing tools used in Indian Cosmetic Industry and its impact on Consumer Behaviour

This research paper investigates the profound influence of Artificial Intelligence (AI) marketing tools on consumer buying behaviours within the burgeoning landscape of the cosmetic industry. With technology becoming increasingly intertwined with beauty and personal care, this study aims to dissect the multifaceted relationship between AI interventions and consumer choices.
 Through a comprehensive review of existing literature, the research seeks to identify patterns and trends in how AI-driven marketing strategies shape the decision-making processes of cosmetic consumers. The study focuses on a spectrum of AI tools employed in the industry, including virtual try-on experiences, personalized product recommendations, and AI-powered chatbots, exploring their impact on consumer perceptions, preferences, and purchasing decisions.
 The descriptive analysis will unveil the nuances of how AI algorithms interpret and respond to individual consumer data, tailoring marketing strategies to align with diverse beauty preferences. Furthermore, the research will explore the role of augmented reality in transforming the cosmetic shopping experience, offering consumers a virtual lens into product usage before making informed choices.
 By shedding light on the intricate interplay between AI marketing tools and consumer behaviours in the cosmetic industry, this paper aims to provide valuable insights for cosmetic brands, marketers, and policymakers. Understanding the dynamics of this relationship is crucial for the industry's stakeholders as they navigate the evolving landscape of beauty and technology, ensuring that AI interventions not only meet consumer expectations but also contribute positively to the overall cosmetic purchasing experience.

Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm

In the realm of personalized product recommendation, addressing the challenges of sparse data and “cold start” has been the primary focus. However, filtering invalid information amidst the overwhelming data on e-commerce platforms remains an underexplored issue. This paper proposes a fusion recommendation algorithm based on frequent item set mining to tackle this problem by compressing the commodity data set and identifying the frequent commodity set. The algorithm not only improves time efficiency by reducing the number of candidate frequent item sets but also generates more accurate recommendations by calculating user-commodity interest rankings and recommending similar products. We first present the existing problems in fusion recommendation algorithms based on frequent item set mining, such as redundant rules, low recommendation accuracy, and the inability to explore deep connections between users and products. Next, we introduce our proposed algorithm, which involves filtering the commodity data set, calculating user-commodity interest rankings, and defining similar product recommendation rules. The algorithm's effectiveness is demonstrated by its ability to adapt to users' dynamic preferences and capture their changing interests in real-time. A comparative analysis using our algorithm and other data mining algorithms reveals a reduction in the number of frequent commodity data sets and weighted frequent item sets, leading to decreased algorithm operation time. This research contributes to the development of more efficient and accurate personalized product recommendation algorithms, enhancing user experience on e-commerce platforms.

An Empirical Study on Personalized Product Recommendation Based on Cross-Border E-Commerce Customer Data Analysis

Thanks to the rapid growth of cross-border e-commerce platforms, numerous cross-border items are now available to customers. Several serious issues with cross-border e-commerce platforms related to item promotion and consumer product screening have arisen. Particular importance should be placed on studying and implementing personalized recommendation systems based on international e-commerce. In light of the quick expansion of commodities, when making individualized suggestions, traditional recommendation algorithms have had to deal with issues such as scant data, a chilly start to the market, and trouble identifying user preferences. To automatically mine the implicit and latent relationships between users and objects in recommendation systems, this study employs deep learning with nonlinear learning capabilities, which resolves the challenges of user interest mining. The weaknesses of the existing global recommendation research are emphasized, the study of conventional recommendation algorithms mixed with deep learning technology is deep factorization machine (DeepFM) and neural matrix factorization (NeuMF) models. Both models excel in recommending implicit feedback data. The DeepFM model yields the lowest loss function values, while the NeuMF model outperforms the competing models in terms of HR@20 (a commonly used indicator to measure the recall rate) and loss functions. In summary, this research addresses critical issues in cross-border e-commerce by developing personalized recommendation systems and integrating deep learning with traditional recommendation algorithms to enhance global recommendations.

The Role of Artificial Intelligence in Shaping Sustainable Consumer Behavior: A Cross-Sectional Study of Southwest, Nigeria

Artificial Intelligence (AI) has become ubiquitous leaving imprints in every facet of life even stronger as the growing number of purchases occur online. Despite the associated benefits of AI, little is still known about the relationship between this powerful tool and sustainability in consumer behaviour. This study was, therefore conducted to assess the importance of AI in influencing sustainable consumer behaviour in Nigeria. Data was collected for the research using a pre-tested, well-structured questionnaire administered to 320 respondents. Data collected were analysed using SPSS version 20 and STATA version 12.0. Results of the analysis showed that the experience of the respondents is relatively high at 9.1±4.58, and the mean number of times purchased per month was 5±2.17 while 49 per cent of the respondents are aware of the use of AI in online shopping. About 67.5 per cent of the respondents were familiar with AI while 27.19 per cent carried out a purchasing decision based on AI-generated recommendations related to sustainability. Consumers also believe that the influence of AI on consumer choices is reflected in receiving personalized recommendations for products and services, but believe AI plays moderate role on the level of influence these personalized recommendations have on the decision to purchase a product is relatively low. The results suggest that AI could impact Sustainable consumer behaviour in the study area.

Research on user personalized product recommendation method of e-commerce platform based on rough set

Research on user personalized product recommendation method of e-commerce platform based on rough set

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.

Skinophile: The Art Of Skincare Selection With Haar Wavelet

In this study, a mobile application has been developed with the primary objective of classifying facial skin types to facilitate the selection of appropriate skincare products tailored to individual skin needs. The motivation behind this research stems from the widespread issue of individuals being unaware of their specific skin types, which often leads to suboptimal skincare routines and, in some cases, adverse effects such as skin dullness and damage. To address this challenge, a comprehensive problem-solving approach has been adopted. The methodology encompasses the utilization of the Haar Wavelets method for facial image feature extraction, complemented by the application of the Support Vector Machine (SVM) algorithm for precise skin type classification. Face Recognition technology has also been integrated to enhance the accuracy and reliability of the classification process.In terms of system development, an Agile methodology has been employed, facilitating swift and cost-effective project completion. This agile approach ensures the development process is efficient, reducing both time and financial resources.The mobile application developed for this purpose utilizes the Python programming language, specifically incorporating the PyWavelets library. The culmination of this research effort is a user-friendly mobile application that enables users to capture their facial images. Subsequently, the application employs advanced algorithms to identify their specific skin type, providing personalized recommendations for suitable skincare products and routines. This innovative solution aims to empower individuals with the knowledge and tools necessary to enhance the effectiveness of their skincare regimen, ultimately promoting healthier and more radiant skin.

Computational Technologies for Fashion Recommendation: A Survey

Fashion recommendation is a key research field in computational fashion research and has attracted considerable interest in the computer vision, multimedia, and information retrieval communities in recent years. Due to the great demand for applications, various fashion recommendation tasks, such as personalized fashion product recommendation, complementary (mix-and-match) recommendation, and outfit recommendation, have been posed and explored in the literature. The continuing research attention and advances impel us to look back and in-depth into the field for a better understanding. In this article, we comprehensively review recent research efforts on fashion recommendation from a technological perspective. We first introduce fashion recommendation at a macro level and analyze its characteristics and differences with general recommendation tasks. We then clearly categorize different fashion recommendation efforts into several sub-tasks and focus on each sub-task in terms of its problem formulation, research focus, state-of-the-art methods, and limitations. We also summarize the datasets proposed in the literature for use in fashion recommendation studies to give readers a brief illustration. Finally, we discuss several promising directions for future research in this field. Overall, this survey systematically reviews the development of fashion recommendation research. It also discusses the current limitations and gaps between academic research and the real needs of the fashion industry. In the process, we offer a deep insight into how the fashion industry could benefit from the computational technologies of fashion recommendation.

Developing a Convenience Store Product Recommendation System through Store-Based Collaborative Filtering

Providing personalized product recommendations in offline retail stores, especially small-format offline retail businesses such as convenience stores, poses a great challenge. To address this issue, this study aimed to find a solution by shifting the perspective on recommendation methods and altering the target of recommendations. In this study, recommending products was defined as suggesting products that should be introduced and displayed within the store. This recommendation system proposes products that individual stores have not yet introduced but are anticipated to be purchased by customers. Building upon this, we developed a store-based collaborative filtering recommendation system. Furthermore, various rules and logic pertinent to store operations and business considerations for convenience stores were integrated to implement this recommendation system. The accuracy and effectiveness of the system were demonstrated through its application in actual convenience stores. Results from the pilot implementation of the system showed that 88% of the newly recommended products in individual stores were sold within a week, and the sales revenue was 1.75 times higher than the average sales of those products across the entire stores. Survey results on business owners’ satisfaction yielded a score of 4.2 out of 5, indicating a high level of contentment. This research holds significance in extending the scope of personalized recommendation studies from primarily online platforms to offline retail businesses such as convenience stores. The study also suggests avenues for future research to address some of the identified limitations.

Research on User Profile Combined with Collaborative Filtering Recommendation Algorithm for Intelligent Tourism

In recent years, the online travel sector in the tourism industry has experienced significant growth and popularity due to the development and widespread adoption of internet technology and smart devices. However, despite these advancements, scenic spots have struggled to provide precise services to tourists, as the online marketplace is flooded with numerous and disorganized commodity resources, lacking standardized construction and systematic management. As a result, travelers find it challenging to access specialty goods that cater to their personalized needs. To address this issue, this paper proposes the use of user profiling and collaborative filtering recommendation algorithms to achieve personalized recommendations for specialty products in scenic spots. The general process of constructing user profiles for scenic spots and combining them with collaborative filtering algorithms to create an intelligent tourism recommendation system is outlined. The paper also highlights the current challenges faced by this system in practical applications and provides future research prospects to promote accurate services in tourist attractions.

Factoring textual reviews into user preferences in multi-criteria based content boosted hybrid filtering (MCCBHF) recommendation system

Recommendation systems help customers to find interesting and valuable resources in the internet services. Their priority is to create and examine users’ individual profiles, which contain their preferences, and then update their profile content with additional features to finally increase the users’ satisfaction. Specific characteristics or descriptions and reviews of the items to recommend also play a significant part in identifying the preferences. However, inferring the user’s interest from his activities is a challenging task. Hence it is crucial to identify the interests of the user without the intervention of the user. This work elucidates the effectiveness of textual content together with metadata and explicit ratings in boosting collaborative techniques. In order to infer user’s preferences, metadata content information is boosted with user-features and item-features extracted from the text reviews using sentiment analysis by Vader lexicon-based approach. Before doing sentiment analysis, ironic and sarcastic reviews are removed for better performance since those reviews inverse the polarity of sentiments. Amazon product dataset is used for the analysis. From the text reviews, we identified the reasons that would have led the user to the overall rating given by him, referred to as features of interest (FoI). FoI are formulated as multi-criteria and the ratings for multiple criteria are computed from the single rating given by the user. Multi-Criteria-based Content Boosted Hybrid Filtering techniques (MCCBHF) are devised to analyze the user preferences from their review texts and the ratings. This technique is used to enhance various collaborative filtering methods and the enhanced proposed MCKNN, MCEMF, MCTFM, MCFM techniques provide better personalized product recommendations to users. In the proposed MCCBHF algorithms, MCFM yields better results with the least RMSE value of 1.03 when compared to other algorithms.

Development of a travel recommendation algorithm based on multi-modal and multi-vector data mining.

Given the rise of the tourism industry, there is an increasing urgency among tourists to access information about various tourist attractions. To address this challenge, innovative solutions have emerged, utilizing recommendation algorithms to offer customers personalized product recommendations. Nonetheless, existing recommendation algorithms predominantly rely on textual data, which is insufficient to harness the full potential of online tourism data. The most valuable tourism information is often found in the multi-modal data on social media, characterized by its voluminous and content-rich nature. Against this backdrop, our article posits a groundbreaking travel recommendation algorithm that leverages multi-modal data mining techniques. The proposed algorithm uses a travel recommendation platform, designed using multi-vector word sense segmentation and multi-modal data fusion, to improve the recommendation performance by introducing topic words. In our final experimental comparison, we verify the recommendation performance of the proposed algorithm on the real data set of TripAdvisor. Our proposed algorithm has the best degree of confusion with various topics. With a LOP of 20, the Precision and MAP values reach 0.0026 and 0.0089, respectively. It has the potential to better serve the tourism industry in terms of tourist destination recommendations. It can effectively mine the multi-modal data of the tourism industry to generate more excellent economic and social value.

Rejoinder on “Competing for Recommendations: The Strategic Impact of Personalized Product Recommendations in Online Marketplaces”

History: Olivier Toubia served as the senior editor.

AN ONTOLOGY BASED PRODUCT RECOMMENDATION SYSTEM FOR NEXT GENERATION E-RETAIL

ABSTRACT The number of e-commerce resources has increased considerably. Thus, it has become important for sellers to be able to quickly recommend products to potential buyers. Some product recommendation systems developed for this purpose. However, due to the lack of semantics, the systems’ success in recommending accurate products according to user preferences is low. In this study carried out within the scope of a state-funded R&D project, an ontology-based personalized product recommendation system named E-Prod was developed. E-Prod tracks various e-commerce systems in real time and transfers the product information to the ontology model. E-Prod uses a novel recommendation approach that combines machine learning and semantic matching to provide personalized recommendations. The system learns user’s preferences based on semantic relationships between products by monitoring their behaviors. In this way, accurate recommendations are made by semantic matching between products and user preferences. E-Prod has been tested with over 250 registered users and compared to traditional collaborative recommendations in terms of accuracy, precision, and recall. As a result, E-Prod outperformed traditional methods by 92.79% accuracy, 92.93% precision, and 90.58% recall. Within the scope of this study, E-Prod covers the clothing, shoes, and bag retail sectors. However, it provides a generic infrastructure for new generation e-commerce systems. Its reusable modules can be adapted to any domain.