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Abstract: The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users. [Copyright &y& Elsevier]

The rapid growth of e-commerce has caused product overload where customers on the Web are no longer able to effectively choose the products they are exposed to. To overcome the product overload of online shoppers, a variety of recommendation methods have been developed. Collaborative filtering (CF) is the most successful recommendation method, but its widespread use has exposed some well-known limitations, such as sparsity and scalability, which can lead to poor recommendations. This paper proposes a recommendation methodology based on Web usage mining, and product taxonomy to enhance the recommendation quality and the system performance of current CF-based recommender systems. Web usage mining populates the rating database by tracking customers'' shopping behaviors on the Web, thereby leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real e-commerce data show that the proposed methodology provides higher quality recommendations and better performance than other CF methodologies. [Copyright &y& Elsevier]

Abstract: The pervasive deployment of P2P (peer-to-peer) systems and the multimedia contents overload in web environment raise a serious complexity for the peers where peers that participate in a P2P network are no longer able to effectively choose the contents they want. Recommender systems have been popularly used for reducing information overload of internet surfers by suggesting products or digital contents that are most valuable for them. But most existing recommender systems have been worked in client–server architecture. This paper proposes a PEOR (PEer-ORiented Recommender system), a collaborative filtering-based multimedia contents recommender system in P2P architecture, to obtain the peers’ search efficiency. To adopt a change in peer preferences PEOR uses recent ratings of peers for recommendations, thereby leading to better quality recommendations. And to enhance the system performance, PEOR searches for nearest peers with similar preference through peer-based local information only. We implemented the system and evaluated its performance with real transaction data in S content provider offering character images. Our experimental data shows that PEOR offers not only remarkably higher quality of recommendations but also the dramatically faster performance than the centralized benchmark system. [Copyright &y& Elsevier]