Product recommendation using typicality based collaborative filtering and churn analysis

2016 ◽  
Author(s):  
Ali Umair ◽  
Zareen Alamgir
Keyword(s):  
Collaborative Filtering ◽  
Product Recommendation ◽  
Churn Analysis

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 An Improved Product Recommendation Method for Collaborative Filtering

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 123841-123857
Author(s):  
Arta Iftikhar ◽  
Mustansar Ali Ghazanfar ◽  
Mubbashir Ayub ◽  
Zahid Mehmood ◽  
Muazzam Maqsood
Keyword(s):  
Collaborative Filtering ◽  
Product Recommendation

A hybrid of sequential rules and collaborative filtering for product recommendation

2009 ◽  
Vol 179 (20) ◽  
pp. 3505-3519 ◽  
Author(s):  
Duen-Ren Liu ◽  
Chin-Hui Lai ◽  
Wang-Jung Lee
Keyword(s):  
Collaborative Filtering ◽  
Product Recommendation
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