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Association Rule Mining for Web Recommendation
R. Suguna,D. Sharmila
International Journal on Computer Science and Engineering , 2012,
Abstract: Web usage mining is the application of web mining to discover the useful patterns from the web in order to understand and analyze the behavior of the web users and web based applications. It is theemerging research trend for today’s researchers. It entirely deals with web log files which contain the user website access information. It is an interesting thing to analyze and understand the user behaviorabout the web access. Web usage mining normally has three categories: 1. Preprocessing, 2. Pattern Discovery and 3. Pattern Analysis. This paper proposes the association rule mining algorithms for betterWeb Recommendation and Web Personalization. Web recommendation systems are considered as an important role to understand customers’ behavior, interest, improving customer convenience, increasingservice provider profits and future needs.
The Application of Book Intelligent Recommendation Based on the Association Rule Mining of Clementine  [PDF]
Jia Lina, Mao Zhiyong
Journal of Software Engineering and Applications (JSEA) , 2013, DOI: 10.4236/jsea.2013.67B006
Abstract:

The traditional library can’t provide the service of personalized recommendation for users. This paper used Clementine to solve this problem. Firstly, model of K-means clustering analyze the initial data to delete the redundant data. It can avoid scanning the database repeatedly and producing a large number of false rules. Secondly, the paper used clustering results to perform association rule mining. It can obtain valuable information and achieve the service of intelligent recommendation.

Integrated Web Recommendation Model with Improved Weighted Association Rule Mining
S.A.Sahaaya Arul Mary,M.Malarvizhi
International Journal of Data Mining & Knowledge Management Process , 2013,
Abstract: World Wide Web plays a significant role in human life. It requires a technological improvement to satisfy the user needs. Web log data is essential for improving the performance of the web. It contains large,heterogeneous and diverse data. Analyzing g the web log data is a tedious process for Web developers, Web designers, technologists and end users. In this work, a new weighted association mining algorithm is developed to identify the best association rules that are useful for web site restructuring and recommendation that reduces false visit and improve users’ navigation behavior. The algorithm finds the frequent item set from a large uncertain database. Frequent scanning of database in each time is the problem with the existing algorithms which leads to complex output set and time consuming process. Theproposed algorithm scans the database only once at the beginning of the process and the generated frequent item sets, which are stored into the database. The evaluation parameters such as support, confidence, lift and number of rules are considered to analyze the performance of proposed algorithm and traditional association mining algorithm. The new algorithm produced best result that helps the developer to restructure their website in a way to meet the requirements of the end user within short time span.
COLLABORATIVE WEB RECOMMENDATION SYSTEMS BASED ON AN EFFECTIVE FUZZY ASSOCIATION RULE MINING ALGORITHM (FARM)  [PDF]
A.KUMAR,,Dr. P. THAMBIDURAI
Indian Journal of Computer Science and Engineering , 2010,
Abstract: With increasing popularity of the web-based systems that are applied in many different areas, they tend to deliver customized informationfor their users by means of utilization of recommendation methods. This recommendation system is mainly classified into two groups:Content-based recommendation and collaborative recommendation system. Content based recommendation tries to recommend web sites similar to those web sites the user has liked, whereas collaborative ecommendation tries to find some users who share similar tastes with the given user and recommends web sites they like to that user. Based on web usage data in adoptive association rule based web mining theassociation rules were applied to personalization. The technique utilize apriori algorithm to generate association rules. Even this method has some disadvantages. To overcome those disadvantages, the author proposed a new algorithm for web recommendation system known as an effective Fuzzy Association Rule Mining Algorithm (FARM). This proposed Fuzzy ARM algorithm for association rule mining in webrecommendation system results in better quality and performance.
A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm  [PDF]
Ujwala H. Wanaskar, Sheetal R. Vij, Debajyoti Mukhopadhyay
Journal of Software Engineering and Applications (JSEA) , 2013, DOI: 10.4236/jsea.2013.68049
Abstract:

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 recommendation 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 association 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 frequency 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 overcome this problem, we have used the web usage log in the adaptive association rule based web mining where the association 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.

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm  [PDF]
Ujwala Wanaskar,Sheetal Vij,Debajyoti Mukhopadhyay
Computer Science , 2013,
Abstract: 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-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-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-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 those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-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.
Granular association rule mining through parametric rough sets for cold start recommendation  [PDF]
Fan Min,William Zhu
Computer Science , 2012,
Abstract: Granular association rules reveal patterns hide in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold start recommendation, where a customer or a product has just entered the system. An example of such rules might be "40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol." Mining such rules is a challenging problem due to pattern explosion. In this paper, we propose a new type of parametric rough sets on two universes to study this problem. The model is deliberately defined such that the parameter corresponds to one threshold of rules. With the lower approximation operator in the new parametric rough sets, a backward algorithm is designed for the rule mining problem. Experiments on two real world data sets show that the new algorithm is significantly faster than the existing sandwich algorithm. This study indicates a new application area, namely recommender systems, of relational data mining, granular computing and rough sets.
A Generalized Association Rule Mining Algorithm for Library New Book Recommendation
一种面向图书馆新书推荐服务的广义关联规则挖掘算法

She Junsheng Huang Zhan,
佘俊胜
,黄战

现代图书情报技术 , 2006,
Abstract: Based on MMS_Cumulate algorithm and GP-Apriori algorithm,a data mining algorithm,MAR_LCR is proposed for library new book recommendation service which is capable of finding generalized association rules in the form of "reader-book" and allows the user to specify multiple minimum supports for different items.The search space is greatly cut down by improving the process of candidate generation.Experiment results show that the MAR_LCR algorithm is highly effective.Finally,a new book recommendation model is proposed.
Mining Web Navigation Profiles For Recommendation System  [PDF]
Y.M. AlMurtadha,Md. N.B. Sulaiman,N. Mustapha,N.I. Udzir
Information Technology Journal , 2010,
Abstract: This study explores web usage mining, for which many data mining techniques such as clustering, classification and pattern discovery have been applied to web server logs. The output is a set of discovered patterns which form the main input to the recommendation systems which in return predict the next web navigations. Most of the recommendation systems are user-centered which make a prediction list to the users based on their long term navigation history, users databases or full users profiles. Companies wish to attract anonymous users, directed them at the early stages of their visits and get them involved with their websites. Learning and mining the web navigation profiles followed by enhanced classification to the similar activities of previous users will provide an appropriate model to recommend to the current anonymous active user with short term navigation. Using CTI dataset, the experimental results show better prediction accuracy than the previous works. An adaptive profiling to save time is a key fac
A Personalized Collaborative Filtering Recommendation Using Association Rules Mining and Self-Organizing Map  [cached]
Hongwu Ye
Journal of Software , 2011, DOI: 10.4304/jsw.6.4.732-739
Abstract: With the development of the Internet, the problem of information overload is becoming increasing serious. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and proceedings coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommendation systems are one such software tool used to help users obtain recommendations for unseen items based on their preferences. The commonly used personalized recommendation system methods are content-based filtering, collaborative filtering, and association rules mining. Unfortunately, each method has its drawbacks. This paper presented a personalized collaborative filtering recommendation method combining the association rules mining and self-organizing map. It used the association rules mining to fill the vacant where necessary. Then, it employs clustering function of self-organizing map to form nearest neighbors of the target item and it produces prediction of the target user to the target item using item-based collaborative filtering. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.
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