Contextual recommender system for E-commerce applications

Khan, Zafran; Hussain, Muhammad Ishfaq; Iltaf, Naima; Kim, Joonmo; Jeon, Moongu

doi:10.1016/j.asoc.2021.107552

Cited by 15 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the discussed problem of missing or insufficient information, it seems interesting to refer to the dimensionality reduction methods based on the granularization of the attribute space [25], and particularly on resilient ML techniques [26], [27] -i.e., resistant to data deficiencies. The hybridization of soft computing techniques with collaborative and content-based methods is a wide-ranging field of research, and an interesting area for the further development of recommendation systems [28], particularly interesting for context-aware RSs [29]- [31].…”

Section: Related Workmentioning

confidence: 99%

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Kannout,

Grzegorowski,

Grodzki

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Recommender systems (RS) are substantial for online shopping or digital content services. However, due to some data characteristics or insufficient historical data, may encounter considerable difficulties impacting the quality of their recommendations. This study introduces the clustering-based frequent pattern mining framework for recommender systems (Clustering-based FPRS) -a novel RS constituting several recommendation strategies leveraging agglomerative clustering and FP-growth algorithms. The developed strategies combine the generated frequent itemsets with collaborative-and content-filtering methods to address the cold-start problem, which occurs whenever a new user or item enters the system. In such cases, the RS has limited information about the new user or object. Thus, the recommendations may be inaccurate. The experimental evaluation on several benchmark datasets showed that Clustering-based FPRS is superior to state-of-the-art and could effectively alleviate the cold-start problem.INDEX TERMS Cold-start problem, recommender system, frequent pattern mining, clustering I. INTRODUCTION

show abstract

Section: Related Workmentioning

confidence: 99%

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Kannout,

Grzegorowski,

Grodzki

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Drug name extraction and recognition from the text for clinical application are performed using BiLSTM, CNN with CRF, and Sence2Vec embedding and achieve an F1-score of 80.30% (Suárez-Paniagua et al 2019 ). The CNN model and Word2Vec embedding create an efficient recommender system for e-commerce applications based on user preferences with an RMSE of 0.863 (Khan et al 2021 ). For a word-level NER test in a language mix of English and Hindi, a multichannel neural network model consisting of BiLSTM and Word2Vec embedding gets an F1-score of 83.90% (Shekhar et al 2019 ).…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

“… Yilmaz and Toklu ( 2020 ) Question classification task on Turkish question dataset Turkish question dataset CNN, LSTM, SVM Word2Vec (CBOW and Skip-Gram) CNN + Skip-Gram achieves an accuracy of 94% 18. Khan et al ( 2021 ) Efficient recommendation system based on user preferences Amazon Instant Videos, Apps for Android, Yelp dataset CNN Word2Vec CNN + Word2Vec achieves an RMSE of 0.863 19. Yang et al ( 2021a ) Product review analysis based on word sentiment Amazon Toy and_Games, Kindle_Store dataset, Yelp-2017 BiLSTM Word2Vec, GloVe BiLSTM + Word2Vec achieves an accuracy of 85.13% 20.…”

Section: Appendix Amentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

View full text Add to dashboard Cite

The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

show abstract

“…They used the sequential behavior of each user as features, and they achieved state-of-the-art performance. Another successful combination of multiple NN components is the architecture proposed by Khan et al [86], in which they applied a CNN for extracting contextual information from textual item descriptions, along with a W2V component for representing items and users. These modules were integrated with a CF component to provide top-N recommendations.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

Stalidis,

Karaveli,

Diamantaras

et al. 2023

Sustainability

View full text Add to dashboard Cite

In recent years, the interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers. The current review focuses on RS related to retail shopping, highlighting recent research efforts towards enhanced e-shops and more efficient sustainable digital marketing and personalized promotion. The reported research was categorized by main approach, key methods, and specialized e-commerce problems addressed, while technological aspects were linked with marketing aspects. The increasing number of papers in the field showed that it has become particularly popular, following the explosive growth in e-commerce and mobile shopping. The problems addressed have expanded beyond the performance of the core algorithms to the business aspects of recommendation, considering user acceptance and impact maximization techniques. Technologies have also shifted from the improvement of classic filtering techniques to complex deep learning architectures, in order to deal with issues such as contextualization, sequence-based methods, and automatic feature extraction from unstructured data. The upcoming goals seem to be even more intelligent recommendations that more precisely adapt not only to users’ explicit needs and hidden desires but also to their personality and sensitivity for more sustainable choices.

show abstract

Contextual recommender system for E-commerce applications

Cited by 15 publications

References 12 publications

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Clustering-Based Frequent Pattern Mining Framework for Solving Cold-Start Problem in Recommender Systems

Impact of word embedding models on text analytics in deep learning environment: a review

Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

Contact Info

Product

Resources

About