Beauty Product Recommendation System using Customer Attributes Information

2021 ◽  
Vol 23 (4) ◽  
pp. 69-84
Author(s):  
Hyojoong Kim ◽  
◽  
Woosik Shin ◽  
Donghoon Shin ◽  
Hee-Woong Kim ◽  
...  
Keyword(s):  
Recommendation System ◽  
Product Recommendation

Online Product Recommendation System by Using Eye Gaze Data

2020 ◽  
Author(s):  
A. B. M. Fahim Shahriar ◽  
Mahedee Zaman Moon ◽  
Hasan Mahmud ◽  
Kamrul Hasan
Keyword(s):  
Recommendation System ◽  
Eye Gaze ◽  
Product Recommendation

Personality-Aware Product Recommendation System Based on User Interests Mining and Metapath Discovery

2020 ◽  
pp. 1-13
Author(s):  
Sahraoui Dhelim ◽  
Huansheng Ning ◽  
Nyothiri Aung ◽  
Runhe Huang ◽  
Jianhua Ma
Keyword(s):  
Recommendation System ◽  
Product Recommendation ◽  
User Interests

scholarly journals Product Recommendation System a Comprehensive Review

2021 ◽  
Vol 1022 ◽  
pp. 012021
Author(s):  
Jatin Sharma ◽  
Kartikay Sharma ◽  
Kaustubh Garg ◽  
Avinash Kumar Sharma
Keyword(s):  
Recommendation System ◽  
Comprehensive Review ◽  
Product Recommendation ◽  
System A

POP Cart: Product Recommendation System by an Agent on a Shopping Cart

2021 ◽  
Author(s):  
Ryosuke Takada ◽  
Kenya Hoshimure ◽  
Takuya Iwamoto ◽  
Jun Baba
Keyword(s):  
Recommendation System ◽  
Shopping Cart ◽  
Product Recommendation

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals Product recommendation system based user purchase criteria and product reviews

2019 ◽  
Vol 9 (6) ◽  
pp. 5454
Author(s):  
Jinyoung Kim ◽  
Doyeun Hwang ◽  
Hoekyung Jung
Keyword(s):  
Morphological Analysis ◽  
Recommendation System ◽  
Product Evaluation ◽  
Shopping Mall ◽  
Product Reviews ◽  
Purchasing Decisions ◽  
Product Review ◽  
Product Recommendation ◽  
Average Value ◽  
Purchase Criteria

<span lang="EN-US">In this paper, we propose a system that provides customized product recommendation information after crawling product review data of internet shopping mall with unstructured </span><span lang="EN-US">data</span><span lang="EN-US">, morphological analysis using Python. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product.</span><span lang="EN-US"> And extracts and analyzes only the review including the purchase criterion selected by the user among the product reviews left by other users. The positive and negative evaluations contained in the extracted product review data are quantified and using the average value, we extract the top 10 products with good product evaluation, sort and recommend to users. And provides user-customized information that reflects the user's preference by arranging and providing a center around the criteria that the user occupies the largest portion of the product purchase. This allows users to reduce the time it takes to purchase a product and make more efficient purchasing decisions.</span>

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