Collaborative Filtering Technology And Its Recommended Areas Of E-commerce Application

With the popularity of the Internet and e-commerce application, consumers enjoy the convenience of shopping on the Internet; on the other hand, they have been in trouble of information overload. It is difficult for them to find their needed products within a mass of product information. Therefore, the recommendation system in e-commerce came into being.In this paper, we made a deep study of recommendation system in e-commerce, and then analysed the status and prospects of the mainstream personalized recommendation techonologies in e-commerce. In this basis, we analysed challenges which collaborative filtering recommendation approach suffered from. And then we proposed an item-association-prediction-based collaborative filtering algoritm (IAPCF) to overcome the shortcomings of the traditional item-based collaborative filtering algorithms. Different from the traditional method, IAPCF algorithm does not use the similarity between items, but the association rules to find the nearest neighbors of target item. The experiment results suggested that IAPCF could provide better recommendation results than the traditional item-based collaborative filtering algorithms.With the expansion of E-Commerce systems, the magnitudes of users and commodities grow rapidly, resulting in the extreme sparsity of user rating data. This situation makes the quality of recommendation systems decreases dramatically. To address this issue, we proposed a collaborative filtering recommendation algorithm based on item rating prediction. The improved algorithm IAPCF -UB we proposed predicted ratings of un-rated item by the IAPCF algorithm, and then the nearest neighbors of target user were calculated with a new similarity measure method. The experiment results suggested that this method could efficiently overcome the extreme sparsity of user rating data and provide better recommendation results than the traditional user-based collaborative algorithms.