Answer:
Assuming that TV was working fine and it stopped working when you restart or after power shutdown. Flat Black or blue screen or snow are all seems to be having HDMI cable sync issues. It can be fixed based on the model of the TV and connectivity it is using.
I would suggest to start with very basic step and should go step by step unless we narrow down the problem.
- Turn off all the components, unplug the cables and plug them back by leaving HDMI cables intact. So this process will reset all the units and may help in renegotiation.
- Plug all the cables and Turn on the TV. Plug all other components like sound receiver and source device and Turn on the power. This may solve the problem.
- If issue still persist. Unplug and Plug back HDMI cables and surrounding components in HDMI path. It may solve the issue.
- If the issue still persists, then change the HDMI cable itself and try rebooting all components.
- If input is changed then it might have caused the issue. So identify the cable from the receiver to the TV. figure out its input and change the TV input to that value and it will work.
- In some TV's if power reset happens, It may revert to TV mode then change the video input based on your TV model.
- If the receiver is connected to TV via Coax then change the channel as per TV guide.
- If the receiver and its component is using AV or HDMI then use TV remote to change the input source until correct feed is found.
- If issue still persist, check the cable with another TV and see if it works. If its not then cable is at fault.
- If the cable is fine then change the TV and try above steps.If it works then TV is at fault.
Explanation:
Answer:
Collaborative filtering
Explanation:
This is one out of five on the Recommender system apart from most popular items, Association and Market Basket based Analysis, Content-based analysis, self and hybrid analysis where we use both content-based and collaborative based approach together. And the Recommender system is a very important topic in Data science. For this question, remember that Collaborative filtering focuses on user and various other user's choices which are mathematically alike to concerned users, and which we find with the study of a large data set. Thus, we can predict from our above study that what are going to be likes of concerned users, and at the item level, whether that item will be liked by the concerned user or not. And this is prediction, and we use this approach in Machine learning these days. For this question, and as mentioned in question the requirements, answer is Collaborative filtering.
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