Product recommendation system for small online retailers using association rules mining

As the usage of the internet grows, more and more information is obtained, thus presenting challenges, especially for users and website owners. Website users often have difficulty finding products or services that are relevant to their needs caused by abundant amounts of products and services delivered on a website. Website owners often find it difficult to convey information about the right products and services to certain target users. Based on the problem given above, we can conclude that a recommendation system approach that can improve personalization on their website is needed. The recommendation system approach must be able to provide navigation on the website to make it more adaptive towards the interests and information needed by the user. This study uses Association Rules formed from Microsoft web access log data by finding visitor patterns based on frequently visited web site pages. From the results of the research conducted, the performance of the method used has a precision value of 0.896, 0.058 recall, and F-measure 0.104. Whereas the measurement of the accuracy value resulted in a performance recommendation of exactly 3%, an acceptable rate of 87%, and 10% incorrect. This research shows that the Association Rules method can increase the effectiveness of website personalization to provide relevant information recommendations for visitors. For further research, it can concentrate on improving existing methods thus website personalization becomes more adaptive.

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Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.39.540 ◽

2010 ◽

Vol 39 ◽

pp. 540-544 ◽

Cited By ~ 3

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.

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