Building Personalized Recommendation System in E-Commerce using Association Rule-Based Mining and Classification

2007 ◽  
Author(s):  
Xi-Zheng Zhang
Keyword(s):  
Association Rule ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Rule Based

scholarly journals Design of Context Cluster and Load Balanced Video Recommendation System in Cloud Computing

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Online Social Networks ◽  
Cloud Storage ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Multimedia Services ◽  
Rule Based ◽  
Selection Technique ◽  
Server Selection ◽  
Network Overhead ◽  
Video Recommendation

Nowadays, in online social networks, there is an instantaneous extension of multimedia services and there are huge offers of video contents which has hindered users to acquire their interests. To solve these problem different personalized recommendation systems had been suggested. Although, all the personalized recommendation system which have been suggested are not efficient and they have significantly retarded the video recommendation process. So to solve this difficulty, context extractor based video recommendation system on cloud has been proposed in this paper. Further to this the system has server selection technique to handle the overload program and make it balanced. This paper explains the mechanism used to minimize network overhead and recommendation process is done by considering the context details of the users, it also uses rule based process and different algorithms used to achieve the objective. The videos will be stored in the cloud and through application videos will be dumped into cloud storage by reading, coping and storing process.

scholarly journals Personalized Recommendation System based Association Rule Mining and Sentiment Classification

2020 ◽  
Vol 9 (4) ◽  
pp. 50-57
Keyword(s):  
Association Rule ◽  
Business Strategy ◽  
Association Rule Mining ◽  
Global Economy ◽  
Recommendation System ◽  
Sentiment Classification ◽  
Automated System ◽  
Personalized Recommendation ◽  
Rule Mining ◽  
Business World

In this emerging global economy, e-commerce is an inevitable part of the business strategy. Moreover, the business world comprises the upcoming entrepreneurs who are unaware of the current trends in marketing. Therefore, a recommendation system is very essential for them. In this paper, a fully automated recommendation system for the upcoming entrepreneurs to become successful in their business is proposed. The system works in three stages. In the first stage, the most transacted product is identified using association rule mining FP growth algorithm. This helps in extracting useful information from the previous transacted data by mining the entire set of frequent patterns. The second stage identifies the most customer preferred company based on review analysis. The multilevel clustering process with the generalization of data review is implemented to achieve an accurate review of the product. It rectifies the problems of shilling attack and gray sheep users commonly seen in single level K-means algorithm by refining the collected data. In the third stage, the reviews are sorted using a polarity shift sentiment classification algorithm. It helps in sort positive and negative reviews thereby rating a company. The top rated company would give the best product. Thus, the best product can be identified. From the experimental analysis, it is understood that the proposed system outperforms the existing recommendation methods. Moreover, this automated system helps the user to get the most accurate result within time. Hence, it would be very beneficial to the upcoming businessmen for flourishing their business in this increasing economic world.

Meta-Rule Based Recommender Systems for Educational Applications

2012 ◽  
pp. 211-231 ◽  
Author(s):  
Vicente Arturo Romero Zaldivar ◽  
Daniel Burgos ◽  
Abelardo Pardo
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Relevant Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Rule Based ◽  
User Data ◽  
Rule Sets ◽  
Rule Set ◽  
Educational Applications

Recommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are available. Educational applications are not popular enough to generate large amount of data. In this context, rule-based Recommendation Systems seem a better solution. Rules can offer specific recommendations with even no usage information. However, large rule-sets are hard to maintain, reengineer, and adapt to user preferences. Meta-rules can generalize a rule-set which provides bases for adaptation. In this chapter, the authors present the benefits of meta-rules, implemented as part of Meta-Mender, a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach to Recommendation Systems.

scholarly journals Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Dan Xiang ◽  
Zhijie Zhang
Keyword(s):  
Association Rule ◽  
Recommendation System ◽  
Real Life ◽  
User Preference ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Fuzzy Association Rules ◽  
Cross Border ◽  
Fuzzy Association Rule ◽  
The Impact

Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.

scholarly journals Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

2021 ◽  
Vol 33 (3) ◽  
pp. 19-34
Author(s):  
Dan Yang ◽  
Zheng Tie Nie ◽  
Fajun Yang
Keyword(s):  
Recommender System ◽  
Association Rule ◽  
Personalized Recommendation ◽  
Temporal Association ◽  
Recommendation Algorithm ◽  
Rule Based ◽  
Temporal Association Rule ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Time Aware

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.

Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering

2010 ◽  
Vol 39 ◽  
pp. 540-544 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Content Based Filtering

With the rapidly growing amount of information available, the problem of information overload is always growing acute. Personalized recommendations are an effective way to get user recommendations for unseen elements within the enormous volume of information based on their preferences. The personalized recommendation system commonly used methods are content-based filtering, collaborative filtering and association rule mining. Unfortunately, each method has its drawbacks. This paper presented a personalized recommendation method combining the association rules mining and collaborative filtering. It used the association rules mining to fill the vacant where necessary. And then, the presented approach utilizes the user based collaborative filtering to produce the recommendations. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.

scholarly journals Personalized Recommendation System based Association Rule Mining and Sentiment Classification

2020 ◽  
Vol 9 (4) ◽  
pp. 50-57
Keyword(s):  
Association Rule ◽  
Business Strategy ◽  
Association Rule Mining ◽  
Global Economy ◽  
Recommendation System ◽  
Sentiment Classification ◽  
Automated System ◽  
Personalized Recommendation ◽  
Rule Mining ◽  
Business World

In this emerging global economy, e-commerce is an inevitable part of the business strategy. Moreover, the business world comprises the upcoming entrepreneurs who are unaware of the current trends in marketing. Therefore, a recommendation system is very essential for them. In this paper, a fully automated recommendation system for the upcoming entrepreneurs to become successful in their business is proposed. The system works in three stages. In the first stage, the most transacted product is identified using association rule mining FP growth algorithm. This helps in extracting useful information from the previous transacted data by mining the entire set of frequent patterns. The second stage identifies the most customer preferred company based on review analysis. The multilevel clustering process with the generalization of data review is implemented to achieve an accurate review of the product. It rectifies the problems of shilling attack and gray sheep users commonly seen in single level K-means algorithm by refining the collected data. In the third stage, the reviews are sorted using a polarity shift sentiment classification algorithm. It helps in sort positive and negative reviews thereby rating a company. The top rated company would give the best product. Thus, the best product can be identified. From the experimental analysis, it is understood that the proposed system outperforms the existing recommendation methods. Moreover, this automated system helps the user to get the most accurate result within time. Hence, it would be very beneficial to the upcoming businessmen for flourishing their business in this increasing economic world.

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