Sentence examples for false recommendations from inspiring English sources

The trust reduces with t1 when y receives false recommendations from a recommender (say node p) located within an appropriate trust length from y. {T_{x,y}}^{ID}=left{begin{array}{c}{T}_{k,n}kern5.25em ;left| TRright|>0kern7.75em kern0.75em begin{array}{cc}delta {T}_{x,y} left t-{t}_1right)&; kern1.75em mathrm{eleft t-{trray}_1right5em kern4.5em end{array}right.

Judging the recommendation: Malicious entities often manipulate the indirect trust calculation by sending false recommendations to lower or increase the recommended trust value of the requesting entity.

Therefore, there are risks that such information is leaked to malicious parties which can lead to severe damage to the user's privacy (e.g. exposure or generating false recommendations) [2]. Figure 1 shows the general architecture of a conventional recommender system and possible ways in which privacy breaches can occur.

A survey of students found that ninety per cent of Chinese applicants submit false recommendations.

When a stock price rose from $1.38 to $4.69 on the strength of his false recommendations, then fell to $1.88 after his manipulation stopped, there were winners and losers.

People have said, for instance, "Why hasn't Sandy Weill been charged?" Well, there is no evidence that Sandy Weill — who I think has epitomized for many people the sort of corporate titan who built this entire system and benefitted from it and therefore should pay a price — there is no evidence that he knowingly encouraged or participated in disseminating any false recommendations.

False recommendation attack falsely sends recommendations to include an untrustworthy node in the cluster functionalities.

We consider the following attacks that affect the trust computation False recommendation attack falsely sends recommendations to include an untrustworthy node in the cluster functionalities.

In order to reduce the false recommendation attack, the proposed system undergoes the misbehaviour verification procedure.

The reason is that with higher deviation threshold, such as 0.5 and 0.6, false recommendation from bad-mouthing nodes having deviation of 60%% are only filtered out which causes legitimate nodes as misbehaving nodes, hence more false positives rate.

Even more insidious, "when people find a claim familiar because of prior exposure but do not recall the original context or source of the claim, they tend to think that the claim is true," as noted a 2005 journal article, "How Warnings about False Claims Become Recommendations," which concluded.