Show-Jane Yen
ABSTRACT
In today's e-commerce environment, Collaborative Filtering (CF) is a widely used algorithm for recommender system, which is to identify the users who have similar preferences to the target user, and to predict the preference of the target user according to the preference ratings of the similar users. However, if the preference ratings of the target user are rare or none, then it cannot effectively identify the users with the similar preferences to the target user.
In order to solve the problem of collaborative filtering, this study uses the implicit rating method to automatically calculate the user preference for the items by using the transaction data of the users, and further constructs an item-to-item, user-to-user, and user-to-item relationships, which can be used to calculate the preference rating for the target user, and recommend the products to the target user. The experimental results also show that the recommendation accuracy of our algorithm is higher than the other algorithms on average.
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Index Terms
- A New Approach for Recommender System
Recommendations
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Recommender system architecture based on Mahout and a main memory database
In this study, we propose a news recommendation system architecture using a main memory database (DB) and Mahout. The user's news preference rate is calculated automatically based on the time the user spends reading news items and their length. While ...
A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis
Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from ...
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