?recommender

recommender 發(fā)音

英：[?r?k??m?nd?]　　美：[?r?k??m?nd?r]

英：　　美：

recommender 中文意思翻譯

recommender 相似詞語短語

1、recommends ─── 推薦；使有優(yōu)勢；被推崇的；建議

2、recommit the same error ─── 再次犯同樣的錯誤

3、recommended dry stacked stone for pools ─── 游泳池推薦干砌石

4、recommended books ─── 推薦書

5、recommending definition ─── 推薦定義

6、recommence study ─── 重新開始研究

7、strongly recommended ─── 強烈推薦；極力推薦

8、precomposes do not edit ─── 預合成不編輯

9、recommendation on ─── 關(guān)于……的建議

10、dose recommandee ─── 推薦劑量

recommender 常見例句（雙語使用場景）

1、Recommender: Interface for providing recommendations. ─── Recommender：用于提供推薦的界面。

2、Please see also my earlier post, "Attacking recommender systems" , that discusses another paper by some of the same authors. ─── 可以看看我更早的一篇博文：《攻擊推薦系統(tǒng)》，討論了來自同幾個作者的一篇文章。

3、However, far from perfect point, the research on personalized recommender technology and systems also remains in the initial stage. ─── 然而就目前而言，個性化推薦技術(shù)和系統(tǒng)的研究還停留在初步階段，遠沒有達到完善的地步，仍然存在很多問題亟待解決。

4、Of course, an uninformed recommender is just a degenerate case and may still be useful. ─── 當然，一個信息不足的推薦系統(tǒng)只是一個比較不理想的情況但可能仍然是有用的。

5、The experiment result justifies that the education resource recommender system and the improvement by clustering is feasible. ─── 實驗結(jié)果證明了本文提出的系統(tǒng)設(shè)計方案和聚類對傳統(tǒng)的個性化信息服務(wù)技術(shù)改進的可行性。

6、more closely the recommender has worked with the candidate, the more examples he or she can share. ─── 推薦人和候選人工作的越緊密，它能給出的事實例子就更多。

7、The first one illustrates the necessity of the scientific literature personalized recommender system and applications on recommender system. ─── 第一部分分析科技文獻個性化推薦系統(tǒng)的必要性以及推薦系統(tǒng)的應(yīng)用現(xiàn)況；

8、As such, a recommender must be able to accommodate the active participation of both the place-owner and visitor. ─── 因此，一個推薦系統(tǒng)必須能夠容納積極參與其中的網(wǎng)站所有者和訪客兩方。

9、So, the recommender is better tasked to take advantage of the wisdom of the place-owner "in the moment. " ─── 因此，推薦系統(tǒng)更好的任務(wù)是在某一刻充分利用店主的智慧。

英語使用場景

Of course, an uninformed recommender is just a degenerate case and may still be useful.

In the false advertisement behavior, advertiser, advertising operator, advertising releaser, and recommender in the false advertisement areCo-tortfeasors and should undertake joint indemnity.

We propose a modeling method of artificial psychology which based on Quantification Theory I and introduce how to apply it in recommender system in this paper.

According to the international usual practice, subjects of false advertisementtort are advertiser, advertising operator, advertising releaser, and recommender inthe false advertisement.

This gives secondary effects like very fast graph algos, recommender systems and OLAP-style analytics that are currently not possible with normal RDBMS setups.

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