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recommender
英：【ˌrɛkəˈmɛndə】美：【ˌrɛkəˈmɛndər】
英：
美：
常见释义：
n.
推荐系统
1、recommends ─── 推荐；使有优势；被推崇的；建议
2、recommit the same error ─── 再次犯同样的错误
3、recommended dry stacked stone for pools ─── 游泳池推荐干砌石
4、recommended books ─── 推荐书
5、recommending definition ─── 推荐定义
6、recommence study ─── 重新开始研究
7、strongly recommended ─── 强烈推荐；极力推荐
8、precomposes do not edit ─── 预合成不编辑
9、recommendation on ─── 关于……的建议
10、dose recommandee ─── 推荐剂量
1、
Recommender
: Interface for providing recommendations. ───
Recommender
：用于提供推荐的界面。
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：《攻击推荐系统》，讨论了来自同几个作者的一篇文章。
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，个性化推荐技术和系统的研究还停留在初步阶段，远没有达到完善的地步，仍然存在很多问题亟待解决。
4、Of course, an uninformed
recommender
is just a degenerate case and may still be useful. ─── 当然，一个信息不足的推荐系统只是一个比较不理想的情况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. ─── 实验结果证明了本文提出的系统设计方案和聚类对传统的个性化信息服务技术改进的可行性。
6、more closely the
recommender
has worked with the candidate, the more examples he or she can share. ─── 推荐人和候选人工作的越紧密，它能给出的事实例子就更多。
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献个性化推荐系统的必要性以及推荐系统的应用现况；
8、As such, a
recommender
must be able to accommodate the active participation of both the place-owner and visitor. ─── 因此，一个推荐系统必须能够容纳积极参与其中的网站所有者和访客两方。
9、So, the
recommender
is better tasked to take advantage of the wisdom of the place-owner "in the moment. " ─── 因此，推荐系统更好的任务是在某一刻充分利用店主的智慧。
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.