Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

Abstract

Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique.

Packet Scheduling for Multiple‐Switch Software‐Defined Networking in Edge Computing Environment

“…In Xiong et al [14] a queueing model was proposed for estimating the performance of the SDN controller with the input of a hybrid Poisson stream of packet-in messages. They also modeled the packet forwarding of OpenFlow switches in terms of packet sojourn time [15].…”

“…Let ( ) denote average waiting time for ( ) until it finally gets the service. Similar to (14) and 15, ( ) can be obtained as follows:…”

“…The process of packet matching. messages are handled with the M/G/1 model and M/M/1 model, respectively, as in the existing researches[14,16].…”

Modeling and Simulation Study of Designer’s Bidirectional Behavior of Task Selection in Open Source Design Process

“…In recommendation system, the sparsity problem usually occurs in the transaction data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, Choi et al [20] suggest a means to derive implicit rating information from the transaction data of an online shopping mall and then propose a new user similarity function, which computes the user similarity of two users if they rated similar items, to mitigate the sparsity problem.…”