Product Recommendation In E-commerce Research Articles

Optimization of E-commerce Product Recommendation Algorithm Based on User Behavior

Optimization of E-commerce Product Recommendation Algorithm Based on User Behavior

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

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

Integrasi Kecerdasan Buatan Dalam Sistem Rekomendasi Produk Untuk E-Commerce

The increasing popularity of e-commerce has driven the development of more sophisticated product recommendation systems to enhance user experience. Within this framework, the incorporation of Artificial Intelligence (AI) has become essential for enhancing the caliber of product suggestions through the examination of user behavioral patterns and product attributes. This paper explores the incorporation of artificial intelligence into e-commerce product recommendation systems. This strategy integrates AI methodologies like machine learning, data mining, and natural language processing (NLP) to produce recommendation systems that are personalized and pertinent. Through in-depth analysis of user data and product information, the system can predict user preferences with higher accuracy, improve user engagement levels, and ultimately increase sales conversions. By combining domain knowledge, technical expertise, and the latest research methodologies, This research offers valuable insights for both e-commerce system developers and artificial intelligence researchers. The integration of artificial intelligence into product recommendation systems represents a critical measure in facilitating growth and fostering innovation within the ever-changing e-commerce landscape.

E-Commerce Recommendation System

In today's digitally driven marketplace, E-commerce Product Recommendation Systems (EPRS) play a pivotal role in enhancing user experience, facilitating decision-making, and boosting sales. This paper offers a comprehensive analysis of various methodologies, techniques, and advancements in EPRS. Beginning with an overview of the significance of personalized recommendations in driving user engagement and satisfaction, the paper delves into the underlying principles and algorithms employed in EPRS, including collaborative filtering, content-based filtering, and hybrid approaches. Furthermore, this research explores the challenges associated with EPRS, such as data sparsity, cold start problem, and scalability issues, along with the strategies and innovations proposed to address these challenges. It examines the role of machine learning, deep learning, and artificial intelligence in improving the accuracy and relevance of recommendations, thereby optimizing user experience and maximizing conversion rates. Moreover, the paper investigates the impact of contextual factors, such as user demographics, browsing history, and social interactions, on recommendation quality and effectiveness. It discusses the ethical considerations and privacy concerns surrounding data collection, user profiling, and algorithmic bias in EPRS implementation, emphasizing the need for transparency, fairness, and user consent. Additionally, this research evaluates the performance metrics and evaluation methodologies used to assess the effectiveness and efficiency of EPRS, including precision, recall, coverage, and serendipity. It highlights the importance of continuous evaluation and refinement of recommendation algorithms to adapt to evolving user preferences and market dynamics. In conclusion, this paper provides valuable insights into the state-of-the-art techniques, challenges, and future directions of E-commerce Product Recommendation Systems. By understanding the intricacies and advancements in this field, businesses can leverage EPRS to enhance customer satisfaction, foster brand loyalty, and drive sustainable growth in the competitive landscape of ecommerce. keyword Recommendation Algorithms,

Heterogeneous Graph Convolutional Network for E-Commerce Product Recommendation With Adaptive Denoising Training

Heterogeneous Graph Convolutional Network for E-Commerce Product Recommendation With Adaptive Denoising Training

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.

A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering

This research proposes and implements an e-commerce product recommendation system that combines Case-Based Reasoning (CBR) and K-Means Clustering algorithms. The main aim of this research is to provide more personalized and relevant product recommendations to e-commerce users. The CBR approach leverages users' transaction history to provide customized recommendations, whereas K-Means Clustering groups users with similar preferences increase the relevance of recommendations. This study assesses the effectiveness of the system by conducting a comprehensive evaluation by comparing system recommendations with actual user preferences. The results of this study reveal that the combined approach of CBR and K-Means Clustering can improve the performance of e-commerce product recommendations, ensure the accuracy of recommendations, and produce a more satisfying shopping experience for users. Although there are limitations in terms of the dataset used and the choice of algorithm parameters, this research makes an important contribution in developing a more adaptive and personalized recommendation system for e-commerce platforms.

FEBench: A Benchmark for Real-Time Relational Data Feature Extraction

As the use of online AI inference services rapidly expands in various applications (e.g., fraud detection in banking, product recommendation in e-commerce), real-time feature extraction (RTFE) systems have been developed to compute the requested features from incoming data tuples in ultra-low latency. Similar to relational databases, these RTFE procedures can be expressed using SQL-like languages. However, there is a lack of research on the workload characteristics and specialized benchmarks for RTFE, especially in comparison with existing database workloads and benchmarks (e.g., concurrent transactions in TPC-C). In this paper, we study the RTFE workload characteristics using over one hundred real datasets from open repositories (e.g. Kaggle, Tianchi, UCI ML, KiltHub) and those from 4Paradigm. The study highlights the significant differences between RTFE workloads and existing database benchmarks in terms of application scenarios, operator distributions, and query structures. Based on these findings, we propose to develop a realtime feature extraction benchmark named FEBench based on the four important criteria for a domain-specific benchmark proposed by Jim Gray. FEBench consists of selected representative datasets, query templates, and an online request simulator. We use FEBench to evaluate the effectiveness of feature extraction systems including OpenMLDB and Flink and find that each system exhibits distinct advantages and limitations in terms of overall latency, tail latency, and concurrency performance.

Application of big data search based on collaborative filtering algorithm in cross-border e-commerce product recommendation.

With the deepening of cross-border e-commerce, the trend of buying and selling goods through the Internet is rising. It is necessary to establish a cross-border e-commerce platform that meets the above functions, and improve the ability to process big data in search. For example, the emergence of large amounts of data can not only help users make choices, but also increase the difficulty of users in choosing. At present, there are many problems in the big data search system in the market, such as inaccurate user personality analysis and low importance of product recommendation. E-commerce is developing rapidly in the new era, and new users are increasing every day. Many researchers invest in finding excellent cross-border e-commerce recommendation system as a business platform. The number of information in cross-border e-commerce shows a rapid growth pattern, and the rapid growth of data and information has seriously affected people's judgment. The big data search system based on collaborative filtering algorithm can meet the product recommendation system of cross-border e-commerce. The user matrix label is an attribute of construction. For the label quantification, the new user preference is the model of building the label, and the concept of weight is added to the label. The collaborative filtering algorithm works based on the created weight label.

Evolving Social Media Background Representation with Frequency Weights and Co-Occurrence Graphs

Social media as a background information source has been utilized in many practical computational tasks, such as stock price prediction, epidemic tracking, and product recommendation. However, proper representation of an evolving social media background is still in an early research stage. In this article, we propose a representation method that considers temporal novelties as well as the fine details of word inter-dependencies. Our method is based on the tf-idf and graph embedding techniques. The proposed method has superiority over other representation methods because it takes the advantage of both the temporal aspect of tf-idf and the semantic aspect of graph embeddings. We compare our method with a variety of baselines in two practical application scenarios using real-world data. In tweet popularity prediction, our representation achieves 5.7% less error and 12.8% higher correlation compared to the best baseline. In e-commerce product recommendation, our representation achieves 17% higher hit-rate and 20% higher NDCG compared to the best baseline.

Use of Hybrid Methods in Making E-commerce Product Recommendation Systems to Overcome Cold Start Problems

Use of Hybrid Methods in Making E-commerce Product Recommendation Systems to Overcome Cold Start Problems

Weighted Ensemble LSTM Model with Word Embedding Attention for E-Commerce Product Recommendation

Weighted Ensemble LSTM Model with Word Embedding Attention for E-Commerce Product Recommendation

Retracted: A Social-aware and Mobile Computing-based E-Commerce Product Recommendation System.

[This retracts the article DOI: 10.1155/2022/9501246.].

An E-Commerce Product Recommendation Method Based on Visual Search and Customer Satisfaction

Recently, extensive attention from researchers has been paid to users referring to product review comments when choosing products while shopping online. These shoppers are also more frequently demanding a visual search facility to identify similar or identical products based on images they input. In this paper, the authors propose a product recommendation method to support a visual product search that combines the similarities of both visual and textual information to recommend products with a high level of satisfaction. The authors first utilize the image-based recognition method to calculate the similarities between user-inputted images and product images based on their SIFT features and the surrounding text. Next, to select satisfying products, the authors perform sentiment analysis on product reviews and combine this with users' repeat purchase behavior to recommend products that have a high level of satisfaction rating. Finally, the authors evaluate and discuss the proposed method using real e-commerce data.

Semantic enhanced Markov model for sequential E-commerce product recommendation

Semantic enhanced Markov model for sequential E-commerce product recommendation

Automatic Product Copywriting for E-commerce

Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying the proposed Automatic Product Copywriting Generation (APCG) system into the JD.com e-commerce product recommendation platform. It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening. For selected domains, the models are trained and updated daily with the updated training data. In addition, the model is also used as a real-time writing assistant tool on our live broadcast platform. The APCG system has been deployed in JD.com since Feb 2021. By Sep 2021, it has generated 2.53 million product descriptions, and improved the overall averaged click-through rate (CTR) and the Conversion Rate (CVR) by 4.22% and 3.61%, compared to baselines, respectively on a year-on-year basis. The accumulated Gross Merchandise Volume (GMV) made by our system is improved by 213.42%, compared to the number in Feb 2021.

A Social-aware and Mobile Computing-based E-Commerce Product Recommendation System.

E-commerce product recommendation system can help users to find their own products quickly from a large number of products. To address the shortcomings of the current e-commerce product recommendation system, such as low efficiency and large recommendation errors, we designed an intelligent recommendation system based on social awareness and mobile computing. The behavioral characteristics of the current e-commerce product recommendation system are analyzed; the e-commerce product recommendation system is built according to the data processing technology of mobile computing, and the key technologies of the e-commerce product recommendation system are designed. The test results show that the proposed system overcomes the shortcomings of the traditional e-commerce product recommendation system, speeds up the speed of users to find the products they really need from a large number of products, improves the accuracy of e-commerce product recommendations, and the error of e-commerce product recommendations is much lower than that of the traditional, which has higher practical application value.

E-commerce Recommender System Using PCA and K-Means Clustering

Recently, recommender system has an important role in e-commerce to market products for users. One of recommender system approach that used in e-commerce is Collaborative Filtering. This system works by providing product recommendations based on products liked by other users who have similar preferences. However, sparse conditions in user data will cause sparsity problems, namely the system is difficult to provide recommendations because of the lack of important information needed. Therefore, we propose an e-commerce product recommendation system based on Collaborative Filtering using Principal Component Analysis (PCA) and K-Means Clustering. K-Means is used to overcome sparsity problems and to form user clusters to reduce the amount of data that needs to be processed. While PCA is used to reduce data dimensions and improve clustering performance of K-Means. The test results using the sports product dataset on the Olist e-commerce show that the proposed system has a lower RMSE value compared to other methods. For the number of neighbors of 10, 20, 30, and 40, our system obtains values of 0.771806, 0.75747, 0.75304, 0.75304, and 0.75270.

A Mixed Strategy of Higher-Order Structure for Link Prediction Problem on Bipartite Graphs

Link prediction tasks have an extremely high research value in both academic and commercial fields. As a special case, link prediction in bipartite graphs has been receiving more and more attention thanks to the great success of the recommender system in the application field, such as product recommendation in E-commerce and movie recommendation in video sites. However, the difference between bipartite and unipartite graphs makes some methods designed for the latter inapplicable to the former, so it is quite important to study link prediction methods specifically for bipartite graphs. In this paper, with the aim of better measuring the similarity between two nodes in a bipartite graph and improving link prediction performance based on that, we propose a motif-based similarity index specifically for application on bipartite graphs. Our index can be regarded as a high-order evaluation of a graph’s local structure, which concerns mainly two kinds of typical 4-motifs related to bipartite graphs. After constructing our index, we integrate it into a commonly used method to measure the connection potential between every unconnected node pair. Some of the node pairs are originally unconnected, and the others are those we select deliberately to delete their edges for subsequent testing. We make experiments on six public network datasets and the results imply that the mixture of our index with the traditional method can obtain better prediction performance w.r.t. precision, recall and AUC in most cases. This is a strong proof of the effectiveness of our exploration on motifs structure. Also, our work points out an interesting direction for key graph structure exploration in the field of link prediction.