基於類神經網路之音樂序列推薦

Abstract

近日，考慮購買的物品之間的順序關係的序列型推薦系統，已經引起了眾多學者的關注。序列紀錄的利用，已經在幾種推薦任務上，被證實能使推薦系統性能達到提升。在自然語言處理（NLP）技術的啟發下，我提出了一種新的基於類神經網絡（NN）的序列型音樂推薦器－CNN-rec。在本文中，我比較了幾種基於類神經網路的序列型推薦系統以及傳統經典的推薦器。驗證結果證明我提出的系統優於傳統系統，並與最先進的基於類神經網路的序列型推薦器有相媲美的性能。

Recently, the next-item/basket recommendation system, which considers the sequential relation between bought items, has drawn attention of researchers. The utilization of sequential patterns has boosted performance on several kinds of recommendation tasks. Inspired by natural language processing (NLP) techniques, we propose a novel neural network (NN) based next-song recommender, CNN-rec, in this paper. Then, we compare the proposed system with several NN based and classic recommendation systems on the next-song recommendation task. Verification results indicate the proposed system outperforms classic systems and has comparable performance with the state-of-the-art system.