E-commerce Recommendation Research Articles

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

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

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

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

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

Quantum Representation based Preference Evolution Network for E-commerce recommendation

Quantum theory, originally developed to explain microscopic physical systems, has recently emerged as a novel conceptual and mathematical framework in information science. This paper applies quantum theory to address challenges in E-commerce recommendation, specifically those involving sequential behavior, aiming to mine effective patterns of preference evolution and more accurately predict user interests. Current recommender systems are limited by the sequence length and underutilize side information such as item attributes. To address these issues, we propose a Quantum Representation-based Preference Evolution Network (QRPEN) for E-commerce recommendations. Unlike traditional methods that focus solely on item-ID, our approach integrates a comprehensive set of side information, including both item-ID and attribute data, at each timestamp. We represent item attributes using quantum superposition states and employ density matrices to describe the probability distribution of same-type attributes. These matrices are then transformed into vectors through a quantum measurement-inspired process and fed into a Quasi-RNN model, enabling parallelization and the modeling of longer sequences. This approach effectively captures the dynamic evolution of user preferences. Experiments on public E-commerce datasets demonstrate that QRPEN achieves competitive performance.

The Effects of E-Commerce Recommendation System Transparency on Consumer Trust: Exploring Parallel Multiple Mediators and a Moderator

Recommendation systems are used in various fields of e-commerce and can bring many benefits to consumers but consumers’ trust in recommendation systems (CTRS) is lacking. Recommendation system transparency (RST) is an important factor that affects CTRS. Applying a three-layered trust model, this paper discusses the influence of RST on CTRS in the e-commerce domain, demonstrating the mediating role of perceived effectiveness and discomfort and the moderating role of consumers’ domain knowledge. We recruited 500 participants for an online hypothetical scenario experiment. The results show that consumers’ perceived effectiveness and discomfort can mediate the relationship between RST and CTRS. Specifically, RST (vs. non-transparency) leads to higher perceived effectiveness ( promoting CTRS) and lower levels of discomfort (which inhibits CTRS), in turn increasing CTRS. Domain knowledge positively moderates the positive impact of RST on perceived effectiveness, while negatively moderating the negative impact of RST on discomfort. Further, gender has a negative impact on CTRS when consumers are purchasing experience products but there is no effect when purchasing search products.

Session context data integration to address the cold start problem in e-commerce recommender systems

Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the ‘cold-start’ problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions’ contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.

Hybrid self-attention BiLSTM and incentive learning-based collaborative filtering for e-commerce recommendation systems

Hybrid self-attention BiLSTM and incentive learning-based collaborative filtering for e-commerce recommendation systems

Enhancing E-Commerce Recommendation Systems with Multiple Item Purchase Data: A Bidirectional Encoder Representations from Transformers-Based Approach

This study proposes how to incorporate concurrent purchase data into e-commerce recommendation systems to improve their predictive accuracy. We identified that concurrent purchases account for about 23% of total orders on Katcher’s, a Korean e-commerce platform. Despite the prevalence of concurrent purchases, existing algorithms often overlook this aspect. We introduce a novel transformer-based recommendation algorithm to process a user’s order history, including concurrent purchases. Each order is represented as a natural language sentence, capturing the order timestamp, product names and their attribute values, their corresponding categories, and whether multiple products were purchased together in a single order (i.e., a concurrent purchase). These sentences form a sequence, which serves as a training dataset for fine-tuning Bidirectional Encoder Representations from Transformers (BERT) with the Next Sentence Prediction objective. We validate our ideas by conducting experiments on Katcher’s platform, demonstrating the proposed method’s improved prediction performance compared to existing recommendation systems, with enhanced accuracy and F1 score. Notably, the normalized discounted cumulative gain (NDCG) showed a significant improvement with a large margin. Furthermore, we demonstrate the beneficial impact of integrating concurrent purchase information on prediction performance.

Sentiment Analysis: Predicting Product Reviews for E-Commerce Recommendations Using Deep Learning and Transformers

Sentiment Analysis: Predicting Product Reviews for E-Commerce Recommendations Using Deep Learning and Transformers

Smart Product Recommendations in Web E-Commerce: Leveraging Apriori Algorithm for Market Basket Analysis

Smart Product Recommendations in Web E-Commerce: Leveraging Apriori Algorithm for Market Basket Analysis

The development and application of a novel E-commerce recommendation system used in electric power B2B sector.

The advent of the digital era has transformed E-commerce platforms into critical tools for industry, yet traditional recommendation systems often fall short in the specialized context of the electric power industry. These systems typically struggle with the industry's unique challenges, such as infrequent and high-stakes transactions, prolonged decision-making processes, and sparse data. This research has developed a novel recommendation engine tailored to these specific conditions, such as to handle the low frequency and long cycle nature of Business-to-Business (B2B) transactions. This approach includes algorithmic enhancements to better process and interpret the limited data available, and data pre-processing techniques designed to enrich the sparse datasets characteristic of this industry. This research also introduces a methodological innovation that integrates multi-dimensional data, combining user E-commerce activities, product specifics, and essential non-tendering information. The proposed engine employs advanced machine learning techniques to provide more accurate and relevant recommendations. The results demonstrate a marked improvement over traditional models, offering a more robust and effective tool for facilitating B2B transactions in the electric power industry. This research not only addresses the sector's unique challenges but also provides a blueprint for adapting recommendation systems to other industries with similar B2B characteristics.

A hybrid recommendation algorithm based on user nearest neighbor model

In the realm of e-commerce, personalized recommendations are a crucial component in enhancing user experience and optimizing sales efficiency. To address the inherent sparsity challenge prevalent in collaborative filtering algorithms within personalized recommendation systems, we propose a novel hybrid e-commerce recommendation algorithm based on the User-Nearest-Neighbor model. By integrating the user nearest neighbor model with other recommendation algorithms, this approach effectively mitigates data sparsity and facilitates a more nuanced understanding of the user-product relationship, consequently elevating recommendation quality and enhancing user experience. Taking into account considerations such as data scale and recommendation performance, we conducted experiments utilizing the Spark distributed platform. Empirical findings demonstrate the superiority of our hybrid algorithm over standalone collaborative filtering algorithms across various recommendation indicators.

The LSTM-EMPG Model for Next Basket Recommendation in E-commerce

Personalized recommendations play a crucial role in the modern e-commerce landscape, enabling businesses to meet customers' evolving preferences and boost sales. As customer preferences change, businesses are realizing the importance of suggesting what customers might want to buy next. However, existing approaches face challenges in capturing sequential patterns in user behavior and accurately utilizing previous purchase information. These challenges can be addressed using Long Short-Term Memory Networks (LSTMs). Nevertheless, LSTMs alone may not fully capture users' repetitive purchase behavior or consider the exact timing of purchases. To account for these limitations, Probabilistic Models such as the Modified Poisson Gamma model (MPG) can be employed. The research reported in this paper proposes and investigates a new approach for the next basket recommendation based on the integration of LSTM with an enhanced Modified Poisson Gamma model to enhance next basket recommendation accuracy in e-commerce. The enhanced model (EMPG) includes a refinement of the MPG model to increase its predictive accuracy, and its recommendations are then integrated with an LSTM network to optimize the LSTM’s predictions. The proposed hybrid LSTM-EMPG model has been evaluated on the Instacart dataset and has produced superior results compared to the Multi-period LSTM, the GRU-based model. DREAM (RNN), and DREAM (LSTM) in terms of predictive accuracy, achieving a higher precision and recall.

Emerging synergies between large language models and machine learning in e-commerce recommendations

This paper explores the integration of large language models (LLMs) into collaborative filtering algorithms to enhance recommendation systems in the e-commerce domain. The proposed approach combines user-based and item-based collaborative filtering with LLMs to improve recommendation accuracy and personalization. Specifically, the study introduces a novel framework called PALR, which leverages LLMs to refine user-item interactions and enrich item representations. PALR utilizes historical user behavior data, such as clicks, purchases, and ratings, to guide candidate retrieval and generate recommended items. This study highlights the importance of integrating LLMs into recommendation systems to deliver more accurate and personalized suggestions, ultimately improving user satisfaction and driving sales in e-commerce platforms.

Product Recommendation in Fashion E-Commerce using Deep Learning and Image Similarity

Product Recommendation in Fashion E-Commerce using Deep Learning and Image Similarity

Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

Visually-aware recommender systems have found widespread applications in domains where visual elements significantly contribute to the inference of users’ potential preferences. While the incorporation of visual information holds the promise of enhancing recommendation accuracy and alleviating the cold-start problem, it is essential to point out that the inclusion of item images may introduce substantial security challenges. Some existing works have shown that the item provider can manipulate item exposure rates to its advantage by constructing adversarial images. However, these works cannot reveal the real vulnerability of visually-aware recommender systems because (1) the generated adversarial images are markedly distorted, rendering them easily detected by human observers; (2) the effectiveness of these attacks is inconsistent and even ineffective in some scenarios or datasets. To shed light on the real vulnerabilities of visually-aware recommender systems when confronted with adversarial images, this paper introduces a novel attack method, IPDGI (Item Promotion by Diffusion Generated Image). Specifically, IPDGI employs a guided diffusion model to generate adversarial samples designed to promote the exposure rates of target items (e.g., long-tail items). Taking advantage of accurately modeling benign images’ distribution by diffusion models, the generated adversarial images have high fidelity with original images, ensuring the stealth of our IPDGI. To demonstrate the effectiveness of our proposed methods, we conduct extensive experiments on two commonly used e-commerce recommendation datasets (Amazon Beauty and Amazon Baby) with several typical visually-aware recommender systems. The experimental results show that our attack method significantly improves both the performance of promoting the long-tailed (i.e., unpopular) items and the quality of generated adversarial images.

Dual-blend insight recommendation system for e-commerce recommendations and enhance personalization

E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the internet. This digital transaction model has revolutionized the way businesses operate and consumers shop. In response to the burgeoning complexity of e-commerce datasets, this work addresses the need for advanced recommendation systems. This work introduces the dual-blend insight recommendation system (DIRS) model for personalized e-commerce recommendation system. The DIRS model involves dataset loading, preprocessing, and feature extraction, enabling training with recurrent neural network (RNN) and Bayesian personalized ranking (BPR) models. Recommendations are generated based on user-defined functions, i.e., location and session, and evaluation metrics such as hit rate (HR) and mean reciprocal rate (MRR) highlight DIRS’s superior performance. The model is evaluated using the Tmall dataset. Results reveal DIRS consistently outperforms alternative algorithms, showcasing its effectiveness in 10k and 20k recommendation sets. This study provides valuable insights into optimizing e-commerce recommendations, emphasizing DIRS as a powerful model for enhancing user experience and engagement.

E-commerce recommender system based on improved K-means commodity information management model

Since the start of the 21st century, there has been a rapid development of internet technology, causing electronic computers and smartphones to become increasingly popular. The e-commerce industry also experiences quick development. However, the recommendation technology of e-commerce progresses slowly, hindering it from keeping up with the changing times. To enhance the efficiency and accuracy of e-commerce recommender systems, this research introduces an e-commerce recommender system that utilizes an enhanced K-means clustering algorithm to manage commodity information. This method combines the K-means algorithm with a genetic algorithm by encoding the genetic algorithm, setting the initial population, defining the fitness function, and configuring other parameters. The results of the test indicated that the K-mean clustering algorithm and fuzzy C-mean algorithm had a recommendation accuracy of 87.9 % and 84.8 % respectively under the test dataset. The highest recommendation accuracy was observed from the improved K-mean clustering algorithm, which was 91.1 %. The convergence rate of the improved K-mean clustering algorithm was faster by 44 % compared to the traditional K-mean clustering algorithm and 73 % quicker than the fuzzy C-mean algorithm. The study's findings demonstrate that the refined K-means clustering algorithm greatly enhances the recommendation proficiency and precision of the e-commerce recommendation system, in comparison to other comparable algorithms. This research can potentially advance the e-commerce industry and stimulate its growth.

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.

E-Commerce Data Mining Analysis based on User Preferences and Assiciation Rules

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems.