Abstract
Accuracy and diversity are two important aspects to evaluate the performance of recommender systems. Two diffusion-based methods were proposed respectively inspired by the mass diffusion (MD) and heat conduction (HC) processes on networks. It has been pointed out that MD has high recommendation accuracy yet low diversity, while HC succeeds in seeking out novel or niche items but with relatively low accuracy. The accuracy-diversity dilemma is a long-term challenge in recommender systems. To solve this problem, we introduced a background temperature by adding a ground user who connects to all the items in the user-item bipartite network. Performing the HC algorithm on the network with ground user (GHC), it showed that the accuracy can be largely improved while keeping the diversity. Furthermore, we proposed a weighted form of the ground user (WGHC) by assigning some weights to the newly added links between the ground user and the items. By turning the weight as a free parameter, an optimal value subject to the highest accuracy is obtained. Experimental results on three benchmark data sets showed that the WGHC outperforms the state-of-the-art method MD for both accuracy and diversity.
Highlights
The explosive growth of the Internet and WWW raises a serious information overload problem: we face too many data and resources to effectively find out the relevant ones by our limited processing abilities
Methods for Comparison For comparison, we present the results of three classical recommendation algorithms: the user-based K-Nearest-Neighbor, item-based K-Nearest-Neighbor, and weighted regularized matrix factorization (WRMF)
Each item receives the same heat from the ground user and average it with the heat from other sources
Summary
The explosive growth of the Internet and WWW raises a serious information overload problem: we face too many data and resources to effectively find out the relevant ones by our limited processing abilities. Search engines are useful tools, by which users can find the relevant information with properly chosen queries They lack the consideration of personalization and return the same results to people no matter what their preferences are. Since the search engines require the keywords extracted by the users themselves, when the users don’t know what they want or their preferences can’t be expressed by keywords, the search engines are of no avail To address these problems, recommender systems rise in response to the proper time and conditions, which do not require specified keywords, instead they use the users’ historical activities and possible personal profiles to uncover their preferences and recommend the relevant items to the users according to their potential interests [1]. The recommendation can be considered as a link prediction problem on web-based user-item bipartite networks [2]
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