Product recommendation for online sales systems based on transaction sessions
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Abstract
The advancement of information technology has influenced and accelerated the growth of many economic fields and industries, including e-commerce. This is one area of information technology that has rapidly developed and gained widespread popularity due to the significant benefits it offers to the community. Today, the need for online shopping is increasing because of its convenience and timesaving, especially for busy people. Historical session data plays an important role in helping sales systems recommend orders to customers that suit their personal preferences. This study proposes models for recommending products on online sales systems based on transaction sessions using memory-based collaborative filtering methods, including user-based and item-based, and model-based collaborative filtering methods including SVD and KNN. The experimental results show that the SVD model has a better rating prediction performance than other techniques. Therefore, it is probably proposed for product recommendations on online sales systems.
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