Personalized Product Recommendation and User Satisfaction

2022 ◽  
pp. 35-67
Author(s):  
Priyadarsini Patnaik
Keyword(s):  
Artificial Intelligence ◽  
Regression Analysis ◽  
Information Processing ◽  
User Satisfaction ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Product Recommendation ◽  
Processing Theory ◽  
Recommendation Algorithms ◽  
Access Information

A recommendation system is a significant part of artificial intelligence (AI) to help users' access information at any time and from anywhere. Online product recommender systems are widely used to recommend products based on consumers' preferences. The traditional recommendation algorithms of recommendation engines do not meet the needs of users in the AI environment when exposed to large amounts of data resulting in a low recommendation efficiency. To address this, a personalized recommendation system was introduced. These personalized recommendation systems (PRS) are an important component for ecommerce players in the Indian e-commerce aspects. Since personalized recommendations are becoming increasingly popular, this study examines information processing theory with respect to personalized recommendations and their impact on user satisfaction. Further, relationships between the variables were examined by conducting regression analysis and found a positive correlation exists between personalized product recommendation and user satisfaction.

scholarly journals The algorithm and the flow: Netflix, machine learning and recommendation algorithms

Intexto ◽  
2019 ◽  
pp. 166-184
Author(s):  
João Damasceno Martins Ladeira
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recommendation System ◽  
Cultural Impact ◽  
Recommendation Algorithms ◽  
Definition Of

This article discusses the Netflix recommendation system, expecting to understand these techniques as a part of the contemporary strategies for the reorganization of television and audiovisual. It renders problematic a technology indispensable to these suggestions: the tools for artificial intelligence, expecting to infer questions of cultural impact inscribed in this technique. These recommendations will be analyzed in their relationship with the formerly decisive form for the constitution of broadcast: the television flow. The text investigates the meaning such influential tools at the definition of a television based on the manipulation of collections, and not in the predetermined programming, decided previously to the transmission of content. The conclusion explores the consequences of these archives, which concedes to the user a sensation of choice in tension with the mechanical character of those images.

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lujie Chen ◽  
Mengqi Jiang ◽  
Fu Jia ◽  
Guoquan Liu
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Conceptual Framework ◽  
Conceptual Model ◽  
External Pressure ◽  
Information Processing Theory ◽  
Content Type ◽  
B2b Marketing ◽  
Processing Theory ◽  
Business To Business

Purpose The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing. Design/methodology/approach A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed. Findings This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing. Originality/value This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

Recommendation of sustainable economic learning course based on text vector model and support vector machine

2020 ◽  
pp. 1-11
Author(s):  
Xiangfei Ma
Keyword(s):  
Support Vector Machine ◽  
Recommendation System ◽  
Comprehensive Analysis ◽  
Information Space ◽  
Vector Model ◽  
Personalized Recommendation ◽  
Support Vector ◽  
Retrieval Accuracy ◽  
Recommendation Algorithms ◽  
Massive Information

The sustainable economic learning course recommendation can quickly find the knowledge information that the user really needs from the massive information space and realize the personalized recommendation to the user. However, the occurrence of trust attacks seriously affects the normal recommendation function of the recommendation system, resulting in its failure to provide users with reliable and reliable recommendation results. In order to solve the vulnerability of the recommendation system to the support attack, based on text vector model and support vector machine, this paper makes a comprehensive analysis of the current research status of the robust recommendation technology. Moreover, based on the idea of suspicious user metrics, this paper has conducts in-depth research on how to design highly robust recommendation algorithms, and constructs a highly reliable sustainable economic learning course recommendation model. In addition to this, this research tests the performance of the system from two perspectives of course recommendation satisfaction and system retrieval accuracy. The experiment proves that the model constructed in this paper performs well in the recommendation of sustainable economic learning courses.

scholarly journals Evaluation of Recommendation System for Sustainable E-Commerce: Accuracy, Diversity and Customer Satisfaction

2020 ◽  
Author(s):  
Qinglong Li ◽  
Ilyoung Choi ◽  
Jaekyeong Kim
Keyword(s):  
Customer Satisfaction ◽  
Deep Neural Networks ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Sustainable Competitive Advantage ◽  
Customer Data ◽  
The Sustainable Development ◽  
Personalized Services ◽  
Recommendation Algorithms ◽  
Positive Effect

With the development of information technology and the popularization of mobile devices, collecting various types of customer data such as purchase history or behavior patterns became possible. As the customer data being accumulated, there is a growing demand for personalized recommendation services that provide customized services to customers. Currently, global e-commerce companies offer personalized recommendation services to gain a sustainable competitive advantage. However, previous research on recommendation systems has consistently raised the issue that the accuracy of recommendation algorithms does not necessarily lead to the satisfaction of recommended service users. It also claims that customers are highly satisfied when the recommendation system recommends diverse items to them. In this study, we want to identify the factors that determine customer satisfaction when using the recommendation system which provides personalized services. To this end, we developed a recommendation system based on Deep Neural Networks (DNN) and measured the accuracy of recommendation service, the diversity of recommended items and customer satisfaction with the recommendation service. The experimental results of is the study showed that both recommendation system accuracy and diversity would have a positive effect on customer satisfaction. These results can further improve customer satisfaction with the recommendation system and promote the sustainable development of e-commerce.

scholarly journals Research on E-Commerce Platform-Based Personalized Recommendation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Zhijun Zhang ◽  
Gongwen Xu ◽  
Pengfei Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Prediction Formula ◽  
Recommendation Algorithm ◽  
Real Dataset ◽  
Recommendation Algorithms ◽  
Filling Method ◽  
System Time ◽  
Different Levels

Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.

scholarly journals Restaurant Recommendation System using Machine Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 1671-1675
Keyword(s):  
Recommendation System ◽  
Online Reviews ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Average Rating ◽  
Hybrid Filtering ◽  
Recommendation Algorithms ◽  
Personalized Recommender System ◽  
Content Based Filtering ◽  
Or Applications

Nowadays a big challenge when going out to a new restaurant or cafe, people usually use websites or applications to look up nearby places and then choose one based on an average rating. But most of the time the average rating isn't enough to predict the quality or hygiene of the restaurant. Different people have different perspectives and priorities when evaluating a restaurant. Many online businesses now have implemented personalized recommendation systems which basically try to identify user preferences and then provide relevant products to enhance the users experience . In turn, users will be able to enjoy exploring what they might like with convenience and ease because of the recommendation results. Finding an ideal restaurant can be a struggle because the mainstream recommender apps have not yet adopted the personalized recommender approach. So we took up this challenge and we aim to build the prototype of a personalized recommender system that incorporates metadata which is basically the information provided by interactions of customers and restaurants online(reviews), which gives a pretty good idea of customers satisfaction and taste as well as features of the restaurant. This type of approach enhances user experience of finding a restaurant that suits their taste better. This paper has used a package called lightfm(the library of python for implementing popular recommendation algorithms) and the dataset from yelp. There are different methods of filtering the data, here we have used Hybrid filtering which is a combination of Content-based filtering (CBF) and Collaborative Filtering (CF). Since the results from Hybrid filtering are far more closer to accuracy than CBF or CF respectively. Then hybrid filtering gives results in the form of personalized recommendations for users after training and testing of the data

Hybrid Collaborative Fusion Based Product Recommendation Exploiting Sentiments from Implicit and Explicit Reviews

2021 ◽  
pp. 2141013
Author(s):  
N Zafar Ali Khan ◽  
R. Mahalakshmi
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Profile ◽  
Personalized Recommendation ◽  
Product Reviews ◽  
Aspect Mining ◽  
Product Recommendation ◽  
Collaborative Recommendation ◽  
Demographic Attributes ◽  
Implicit And Explicit

Product recommendation is an important functionality in online ecommerce systems. The goal of the recommendation system is to recommend products with has higher purchase success ratio. User profile, product purchase history etc. have been used in many works to provide high quality recommendations. Product reviews is one of the important source for personalized recommendation. Typical collaborative recommendation systems are built upon user rating on products. But in many cases, these rating information are inaccurate or not available. There is also a problem of biased reviews decreasing the accuracy of recommendation systems. This work proposes a aspect mining collaborative fusion based recommendation system considering both the implicit and explicit reviews. The sentiments about different aspects mined from reviews are translated to multi-dimensional ratings. These ratings are then fused with user profile and demographic attributes to improve the quality of recommendation. The proposed recommendation system has 3.79% lower RMSE, 4.51% lower MAE and 22% lower MRE compared to most recent collaborative filtering based recommendation system.

scholarly journals Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources

2022 ◽  
Vol 2146 (1) ◽  
pp. 012007
Author(s):  
Yu’e Liu
Keyword(s):  
Deep Learning ◽  
Resource Management ◽  
Human Resource ◽  
Management System ◽  
Recommendation System ◽  
Pivotal Role ◽  
Personalized Recommendation ◽  
High Quality ◽  
Product Recommendation ◽  
New Type

Abstract Resource recommendation system is a new type of management system, which uses personalized information to solve business needs such as customer consultation and product recommendation, and provides users with high quality services and achieves accurate marketing, so nowadays resource recommendation system has a pivotal role in modern resource management. In this paper, I study the algorithm and model of resource personalized recommendation based on deep learning, taking human resource recommendation as an example.

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