However, little is known about what criteria users employ when making relevance judgments and which textual representations of the image help them make relevance judgments in their situational context.
To set thresholds for criteria, users drag bars to mark the desired intervals.
The criteria for success or fail may be different from the criteria users might apply and for this reason all validation data can be accessed from PrimerBank.
These specifiers are all strung together in an AND fashion, so a result will only appear if it matches all of the criteria users have given.
A maximum entropy multi-criteria user equilibrium (ME-MUE) model is presented to address the non-uniqueness of the solution in MUE-TRD model.
OD matrices are assigned in the network according to a user defined criterion (user equilibrium -UE, system optimum -SO).
For the second criterion, users are given different weights, and power allocation is performed to maximize the weighted-sum of rates for all users.
This paper proposes a multi-criteria user equilibrium model considering travel time, travel time reliability and distance (MUE-TRD).
Then, we combine the crowdsourced multi-criteria user ratings into a single rating using NNRA and PWRA. is the most meaningful of the multiple ratings available.
In terms of the accuracy of the rating predictions, these results show that: (1) NNRA and PWRA are preferable to MRR profiling and (2) PWRA, when faced with null multi-criteria user ratings, outperforms both MMR and NNRA profiling in both datasets.
In terms of the accuracy of the rating predictions, these results show that: (1) NNRA and PWRA are preferable to MRR profiling; (2) PWRA, when faced with null multi-criteria user ratings, outperforms both MMR and NNRA profiling; (3) trust modelling influences positively the final recommendations and, together with PWRA, is the best approach.
