As with UBSW, the algorithm takes in a sequence of user sessions L and, for each new session, recalculates an item vs. item similarity matrix S using a window with the latest n sessions.
This algorithm takes in a sequence of user sessions L={s1,…},…}.
We see our findings as demonstrating that it is feasible to carefully design and execute a sequence of PD sessions around the medium of video.
The patients selected for the study were booked for PDT and underwent a sequence of four PDT sessions, scheduled 3 4 weeks apart.
The sliding window approach basically considers the n most recent sessions to build the similarity matrix S. To illustrate, consider a sequence of the first n user sessions {s1,…2,…,s n }, each containing a set of items rated by one user.
The sequence of sessions was thus STTSSTTSSTTSSTTSSTTSSTTS, where 'S' represents a session of scene learning and 'T' represents a session of strategy implementation testing.
It shows a simple page of a RIA that displays a description of different products using AJAX (left), and a sequence of generated requests/responses during a session (right).
The sequence of sessions observed was reported on video clips and can be listed as follows (Figure 3 and Figure 4): 1.
IIV changed with time within sessions (model ii): PD i for hyperactivity was negatively correlated with the sequence of sessions over time in both strains (p < 0.0001; β = -1.17 ± 1.07).
An FTP session is a sequence of file transfers separated by reading time D. The two main FTP session parameters are size S of a file to be transferred and reading time D, i.e., the time interval between the end of download of the previous file and the user request for the next file.
