A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm

Abstract

<p class="MsoNormal"> <span lang="EN-US">As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen</span><span lang="EN-US">dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso</span><span lang="EN-US">ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre</span><span lang="EN-US">quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages that are not yet visited by users is not included in the recommendation set. To over</span><span lang="EN-US">come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso</span><span lang="EN-US">ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.</span><span lang="EN-US"><o:p></o:p></span> </p

A Public Cloud Based SOA Workflow for Machine Learning Based Recommendation Algorithms

“…Both algorithms take the transaction data of user purchases as input based on which each algorithm individually makes predictions on what the user might buy next. Although both algorithms try to find frequently occurring patterns in the dataset, they employ different methodologies and hence come up with slightly different results [12]. The results of each algorithm are presented in the experimental results section.…”

Hybridizing association rules with adaptive weighted decision fusion for personal credit assessment

“…They are used to mine the correlation between valuable data items from a large amount of data (Jan, 2015). The AC technique (Wanaskar, Vij, & Mukhopadhyay, 2013;Yue & Shi, 2017) employs association rules in the classification process to increase classification accuracy, in which association rules CONTACT Yue Zhang zhangyue@ahpu.edu.cn are generally mined by making use of a priori-based algorithms (Apilettia et al, 2017;Jorge, Marcio, & Mario, 2018;Xie et al, 2019) or their improvements, say, FP-growthbased algorithms (Pei, Wang, & Wang, 2016). These algorithms aim to find all item sets and association rules whose support, confidence or weighted Chi-square are respectively greater than the minimum counterparts that are specified by users according to the applied database.…”

Machine Learning Algorithms for Recommender System - a comparative analysis

“…A higher rating denotes a higher likelihood of the user to visit similar items. So, a new item is recommended according to the maximum number of ratings given by the user in a genre [3].…”