scholarly journals Enhanced Recommendation System for E-commerce Applications

2019 ◽  
Vol 8 (9) ◽  
pp. 473-478
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Web Applications ◽  
Online Shopping ◽  
User Behavior ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
Special Subset ◽  
And Behavior

Recommendation algorithms play a quintessential role in development of E-commerce recommendation system, Where in Collaborative filtering algorithm is a major contributor for most recommendation systems since they are a flavor of KNN algorithm specifically tailored for E-commerce Web Applications, the main advantages of using CF algorithms are they are efficient in capturing collective experiences and behavior of e-commerce customers in real time, But it is noted that , this results in the phenomenon of Mathew effect, Wherein only popular products are listed into the recommendation list and lesser popular items tend to become even more scarce. Hence this results in products which are already familiar to users being discovered redundantly, thus potential discovery of niche and new items in the e-commerce application is compromised. To address this issue , this paper throws light on user behavior on the online shopping platform , accordingly a novel selectivity based collaborative filtering algorithm is proposed with innovator products that can recommend niche items but less popular products to users by introducing the concept of collaborative filtering with consumer influencing capability. Specifically, innovator products are a special subset of products which are less popular/ have received less traction from users but are genuinely of higher quality, therefore, these aforementioned products can be captured in the recommendation list via innovator-recognition table, achieving the balance between popularity and practicability for the user

CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

2010 ◽  
Vol 21 (10) ◽  
pp. 1217-1227 ◽  
Author(s):  
WEI ZENG ◽  
MING-SHENG SHANG ◽  
QIAN-MING ZHANG ◽  
LINYUAN LÜ ◽  
TAO ZHOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
High Accuracy ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
High Diversity ◽  
Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

scholarly journals Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhenning Yuan ◽  
Jong Han Lee ◽  
Sai Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Preferences ◽  
Classification Model ◽  
Filtering Algorithm ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Low Dimensional ◽  
Preference Matrix

Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.

An Electronic Commerce Recommender System Based on Product Character

2011 ◽  
Vol 267 ◽  
pp. 909-912 ◽  
Author(s):  
Shen Bao Chen
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Competitive Environment ◽  
Referral Process ◽  
Personalized Service ◽  
Filtering Algorithm ◽  
Customer Churn ◽  
Collaborative Filtering Algorithm

In the increasingly competitive environment, in order to effectively preserve the user, preventing customer churn, increase sales of e-commerce systems, e-commerce recommendation system in the importance of the products has been revealed. Recommendation system in e-commerce system can provide commodity information and advice to help customers decide what products to buy, analog sales staff to complete the purchase of goods to the customer referral process so that customers feel completely personalized service. To improve the item-based collaborative filtering algorithm, an electronic commerce recommendation system based on product character is presented. This approach revises the original similarity using product character, takes into account the influence of product character and customer rating, and combines the customer rating similarity and the product character similarity.

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