本文選題：推薦系統(tǒng) + 矩陣因子分解��； 參考：《計(jì)算機(jī)應(yīng)用研究》2017年02期

【摘要】：在推薦系統(tǒng)中,因評(píng)分尺度差異而造成的偏差問(wèn)題一直影響著協(xié)同過(guò)濾算法的預(yù)測(cè)準(zhǔn)確性。其中針對(duì)矩陣因子分解算法中的偏差問(wèn)題,提出一種基于高階偏差的因子分解機(jī)算法。該算法首先按照評(píng)分偏差的現(xiàn)實(shí)特征對(duì)用戶(hù)和項(xiàng)目進(jìn)行劃分,再將偏差類(lèi)別作為輔助特征集成到因子分解機(jī)中,實(shí)現(xiàn)了評(píng)分預(yù)測(cè)中不同偏差用戶(hù)、項(xiàng)目的高階交互。在MovieLens數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果表明,相比傳統(tǒng)矩陣因子分解算法,提出的算法具有更低的預(yù)測(cè)誤差,體現(xiàn)了其更好的推薦性能。
[Abstract]:In the recommendation system, the deviation caused by the difference of scoring scale has always affected the prediction accuracy of collaborative filtering algorithm. In order to solve the deviation problem in matrix factorization algorithm, a factorizer algorithm based on higher order deviation is proposed. In this algorithm, users and items are divided according to the actual features of scoring bias, and then the deviation categories are integrated into the factoring machine as auxiliary features to realize the high-order interaction between users and items with different deviations in scoring prediction. The experimental results on the MovieLens dataset show that the proposed algorithm has lower prediction error than the traditional matrix factorization algorithm, which reflects its better recommendation performance.
【作者單位】： 上海大學(xué)管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(11201290,61104042)
【分類(lèi)號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

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本文編號(hào)：1791144