walman wayamba giacynta
Kerrie Gingrich
I have had a similar issue issue for 4 days except that I see everything but my queue, recently watched and new arrival categories. Very frustrating as this suddenly happened with no changes to any of the setup in my home and affects 2 ATV's the exact same way. I spent 20 minutes with Netflix support on phone and got nowhere.
My issue was limited to yesterday afternoon. It worked fine before that, and now seems to be back to normal. I think it must've been a Netflix problem, and whatever it was, they seem to have cleared it up.
I've already tried the hard reboot and rebooting router and modem too. Also tried logging in and out of netflix. And resetting ATV. No luck on any. I feel it has to be something on the Netflix end since it effects both of my ATV units and nothing has been changed or updated on any of my network or devices to trigger this. I also had the general no connection at all issue last night several other users have reported. I've been at work all day so I'll have to wait to check again till I get home.
I am having the exact same issue. It just started a few days ago. I have tried everything I can think of to fix the problem including all of the things mentioned here. Has anyone figured out a solution?
Same thing out here in Hawaii. Netflix is not the same experience when I can't browse from the sofa with a remote in my hand. Besides, the Netflix queue on Apple TV is kind of a hassle for navigation (which is my only option at this point). After reviewing a title in the list, the "cursor" automatically jumps to the top of the queue when I use the "back" button! Not a problem if you only have a short list, but I have nearly 100 titles there, so I spend a bit of time scrolling to where I left off.
I have had a similar issue. It started a couple of days ago. On the ATV, I try clicking on Netflix, but nothing happens. I have tried rebooting, doing a factory reset, resetting my router, etc... I called Apple and they said that I needed to pay $30 in order to speak to a representative. I have an internet connection because I can go to youtube or access the iTunes store. Does anyone know how to fix this?
I can get thru the netflix menus, but then am told I am not connected to netflix, error 104. I can see things fine on my desktop (ahrd-ethernet connection) and I can see youtube on my apple TV. Same problem with MLB.
The Netflix Prize Challenge Competition ran for 34 months from October 2, 2006 until July 26, 2009. Netflix supplied a "Training" dataset of 100,480,507 ratings made by 480,189 Netflix clients for 17,770 movies between October, 1998 and December, 2005. The ratings were on a rating scale of one star to five stars. The Training data matrix has 99% missing data. Netflix also supplied a dataset of 2,817,131 "Qualifying" ratings. For these ratings, the clients and movies are known, but the actual ratings were known only to Netflix (until the competition concluded). The Netflix Prize was awarded to the team most successful at "predicting" those publicly unknown ratings. Teams were allowed to submit multiple prediction datasets.
Team "BellKor's Pragmatic Chaos" won the Prize in a tie-break based on a 20-minute earlier submission time of their winning prediction dataset. The runners-up were team "The Ensemble" (of which I was a member). The team that had the "dubious honors" (according to Netflix) of the very worst predictions, out of the 44,014 valid submissions from 5,169 actively participating teams, was team "Lanterne Rouge" of which I was the leading member. Of course, these worst predictions were deliberate!
When stored as a rectangular text data-file, the size of the Training dataset is at least 480,189 * 17,770 bytes = 8,532,958,530 bytes = 8GB. When implemented in Winsteps, this required 8GB of input data file and two more work files of the same size = 24 GB (at least). But 99% of these 24GB are missing data. So this was highly inefficient. Simultaneously, Winsteps users were noticing the same thing for their computer-adaptive-testing (CAT) and concurrent-test-equating analyses. In contrast, the same data in Facets (for which missing data do not need to be stored) have an input dataset size of 1.3GB and a work file size of 0.6GB. So obvious improvements to Winsteps were to allow Winsteps to use a Facets-style input-data-format, and to use a compressed work-file algorithm. This reduced the Winsteps input dataset size to 1.3GB and the work-file sizes reduced to 3.3GB and 0.2GB, a total of 5GB instead of 24GB.
The first run of Winsteps on the Training dataset indicated that the estimation process would take 8 days to come to convergence. Consequently that first run was cancelled after 1 day as entirely impractical. The first run in Facets on the same data required about 24 hours. Again this suggested improvements could be made to Winsteps. Reducing the dataset disk-size also reduced the input-output overhead, so reducing processing time. But inspection of the computer code also revealed routines which could be made faster. Consequently Winsteps processing time was reduced to about 12 hours, and much less if only rough convergence is required.
Each time a dataset of predictions was submitted to Netflix, Netflix responded with a summary statistic on the accuracy with which the "Quiz" half of the qualifying ratings had been predicted. Competitors did not know which of the Qualifying ratings comprised the Quiz dataset. The other half of the Qualifying ratings were termed the "Test" dataset. The summary statistic for the Quiz dataset was the root-mean-square-residual (RMSR), called by Netflix the root-mean-square-error (RMSE), between the known-to-Netflix values of the ratings and their competitor-predicted values. The values of the RMSRs enabled competitors to know which of their prediction models were more effective. Netflix permitted submissions to included predictions between categories, such as 3.5674 stars. This improved RMSRs relative to predicting exact categories.
As the competition proceeded, it became apparent that the data were severely multidimensional, and that a unidimensional Rasch analysis was a useful first-stage leading on to other analyses. But, as implemented in Winsteps and Facets, the Andrich Rating Scale model requires the computation of 4 exponentials for each observation in each estimation iteration as well as the accumulation of probabilities for the five categories. Further the threshold estimates need to be brought to convergence. If this processing load could be lessened, without severely impacting the utility of the Rasch measures, then the duration of the first-stage Rasch analysis would be considerably reduced.
Since the Netflix criterion for accuracy of prediction was the RMSR, incorrectly predicting that an observation would be 1 or 5 Stars was considerably worse, on average, than incorrectly predicting that an observation would be in an intermediate category. The use of the extreme 1- and 5-Star categories by Netflix clients was somewhat idiosyncratic. An improvement to prediction resulted when the influence of the extreme categories was reduced. For the DFM model, experiments revealed that better inferences were obtained by substituting to 4.75 Stars (in place of 5 Stars) and 1.25 Stars (in place of 1 Star), and adjusting the probabilities accordingly. For the RSM model (as implemented in Winsteps), this is done by adjusting observed category frequencies. For 5 Stars, the observed rating-score was reduced from 5.0 to 4.75, and the corresponding observed category frequencies were changed from 1 observation of 5 into 0.75 observations of 5 and 0.25 observations of 4. Similarly for 1 Star.
The estimation of RSM rating-scale thresholds has long been troublesome. The original JMLE technique, proposed in "Rating Scale Analysis" (Wright and Masters, 1982) estimated each threshold using Newton-Raphson iteration, as though it was an almost separate parameter. This technique proved too unstable when category frequencies were very uneven or there were pernicious patterns of missing-data. So Newton-Raphson iteration of the threshold estimates was replaced in Winsteps by "Iterative curve-fitting", because the relevant functions are known to be smoothly monotonic logistic ogives.
For the Netflix data, a faster-converging estimation method for rating-scales was sought. An iterative approach based on solving simultaneous linear equations has proved effective. Suppose that Pk is the expected frequency of category k in the dataset according to RSM.
At the end of each iteration, all the numerical values of the Ok,Pk and ΣPnikPnih terms are known.Consequently the Ok equations become a set of simultaneous linear equationswhich can be solved for δFj. Then Fj+δFjbecome the values of Fj for the next iteration after standardization so that Σ Fj = 0.So far, this estimation technique has proved robust and fast.
Multidimensionality is a serious threat to the validity of unidimensional Rasch measures. It also degrades the capability of the measures to predict observations. Single-parameter fit statistics (such as INFIT, OUTFIT and point-biserial correlations) are insensitive to pervasive multidimensionality. PCA of residuals is a useful tool for investigating multidimensionality, but it loses its power as the proportion of missing data increases, and the number of variables to be factored increases. With 99% missing data and 17,770 variables, PCA of residuals is almost ineffective. It does signal the existence of secondary dimensions, but not in enough detail to be useful for item selection or improved prediction.
SVD is mathematical technique that has been used for decomposing matrices into a bilinear form for over 130 years. It is robust against missing data and the size of the matrix to be decomposed, so it is ideal for this application. SVD was the first conspicuously successful multi-dimensional method used by Netflix competitors. Most of those applied it using raw-score models.
Notice that Rasch residuals are explained, as far as possible, by two factors (U for movies and V for clients) which multiply together. The factor products center on zero, because the residuals sum to zero.
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