scholarly journals PRODUCT RECOMMENDATION SYSTEM USING OPINION MINING

2017 ◽  
Vol 4 (01) ◽  
Keyword(s):  
Recommendation System ◽  
Opinion Mining ◽  
Product Recommendation

Product Recommendation System using Opinion Mining

2019 ◽  
Vol 67 (3) ◽  
pp. 66-69
Author(s):  
Ajith Kumar.V ◽  
Arun B ◽  
Balamurugan J ◽  
Nancy Deborah.R
Keyword(s):  
Recommendation System ◽  
Opinion Mining ◽  
Product Recommendation

Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review

2017 ◽  
Vol 44 (3) ◽  
pp. 331-344 ◽  
Author(s):  
Youdong Yun ◽  
Danial Hooshyar ◽  
Jaechoon Jo ◽  
Heuiseok Lim
Keyword(s):  
Collaborative Filtering ◽  
Quantitative Data ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Opinion Mining ◽  
Recommendation Systems ◽  
Product Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Product Rating ◽  
Positive Recommendation

The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users’ actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: ‘Why is collaborative filtering algorithm not effective?’ and ‘Do quantitative data such as product rating or purchase history reflect users’ actual tastes?’

scholarly journals Product Recommendation System based on User Trustworthiness & Sentiment Analysis

2020 ◽  
Vol 32 ◽  
pp. 03030
Author(s):  
Gunjeet Kaur Soor ◽  
Amey Morje ◽  
Rohit Dalal ◽  
Deepali Vora
Keyword(s):  
Sentiment Analysis ◽  
Recommendation System ◽  
Opinion Mining ◽  
Product Recommendation ◽  
Fake Reviews ◽  
New System

The current online product recommendation system based on reviews has many limitations due to randomness in the review patterns. The data which is used are the reviews and ratings from the e-commerce websites. This data might contain fake reviews that make the data uncertain. Due to this, the currently existing systems produce ambiguous results on this present data. Instead of this, the new system uses only genuine reviews, considering the trustworthiness of the user and generates the results in a more significant manner. The proposed system scrapes reviews from different online websites and performs opinion mining and sentiment analysis on it. Other factors like star ratings, the buyer’s profile and previous purchases and whether the review has been given after purchasing or not are included. Based on these factors & user trustworthiness, the website from which the user should buy the product will be recommended.

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

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.

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