結合成對學習與隱含狄利克雷分布之項目推薦

Abstract

現今的網路服務或電子商務提供了非常多的商品與選擇，也因此當使用者在搜尋想要的商品時，有效的推薦系統是非常重要的。在本文中，我們提出了一種基於圖形理論模型的推薦方法，將成對學習的概念納入隱含狄利克雷分布模型中，以挖掘使用者對於項目優先級的偏好。再透過投票機制將使用者對項目的偏好進行積分排序，最後產生出針對每位使用者的個人化推薦清單。由真實世界之數據資料的實驗表明，我們的模型所得出的使用者偏好在項目的推薦中是有效的。此外，結合成對學習的方法在推薦的準確度方面也成功地增強了基於隱含狄利克雷分布的推薦系統。

Internet e-services now provide so many items that effective recommendation systems are crucial to users to search for desire items. In this paper, we present a new recommendation method which is based on theoretical graphical models. We incorporate the concept of pairwise learning into the latent Dirichlet allocation model to discover user preferences which differentiate users’ precedence on items. A voting mechanism applied to the learned user preferences is devised so that favorite items are suggested to the users. Preliminary experiments based on a real-world dataset demonstrate that the discovered user preferences are effective in item recommendations. Also, incorporating pairwise learning successfully enhances the LDA based recommendation method in terms of the recommendation precision.