基于深度網(wǎng)絡(luò)的推薦系統(tǒng)偏置項(xiàng)改良研究

中圖分類號： TP391
文獻(xiàn)標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.08.007
引用格式： 張?zhí)煳? 基于深度網(wǎng)絡(luò)的推薦系統(tǒng)偏置項(xiàng)改良研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(8)：42-46.

Research on improvement of bias in recommendation system based on deep neural network

Abstract： Traditional matrix factorization algorithm sets bias for users and items independently, without digging into the latent preferences of specific users for specific items. As in traditional ranking prediction recommendation algorithm, all the rated items of a user are uniformly set as the user′s positive items(regardless of whether the user gives a good or a bad review). As a result, the trained system is likely to continue to recommend projects that users hate in practical applications. Therefore, an improved bias improvement scheme of recommendation system based on deep neural network is proposed, which takes into account the liking and disliking behind the ratings made by users for specific items, and a joint bias is learned for the recommendation process. The results of comparative experiments on three open datasets show that the improved scheme can effectively improve the recommended performance.

Key words : recommendation algorithm；collaborative filtering；deep neural network；implicit feedback