This paper was fairly short and not too related to our project but it turned out to be very interesting. The basic principle is labeling clips and storing them in a way for easy retrieval. The labeling aspect is very important. If you do not label one of your clips correctly, then it will not show up when you want it.
The idea is to create a movie based on a text file. The user types commands directly into text and it controls the movie clip and what the movie clip does. For example if you type discover a giant rock, it will shot a picture of a giant rock. If you type [close up] it will zoom on on the picture of the rock. In this manner you are able to create many different combinations of a movie novel.
This blog is written about the following paper:
Textable movie: improvising with a personal movie database
http://dl.acm.org/citation.cfm?id=965400.965575
Tuesday, February 28, 2012
Tuesday, February 14, 2012
Image Stabilization for 2D Barcode in Handheld Devices
This paper talked about image stabilization for bar-code scanners for mobile devices. The current problem is most mobile devices are not made to take a close up picture. They are also not very good at anti shake features either.
It address this fact and uses a few different methods to help recreate the bar-code from a blurry picture or even a picture that is taken at an angle. The process they use is first take the picture, then restore the QR code image, then convert it to a binary image, then use edge detection, then dilate the image, then create a block mapping of the image, then produce the output. They can accomplish this by the ratio of white to black in the image.
Image stablization for 2D barcode in handheld devices
http://dl.acm.org/citation.cfm?id=1291233.1291394
It address this fact and uses a few different methods to help recreate the bar-code from a blurry picture or even a picture that is taken at an angle. The process they use is first take the picture, then restore the QR code image, then convert it to a binary image, then use edge detection, then dilate the image, then create a block mapping of the image, then produce the output. They can accomplish this by the ratio of white to black in the image.
Image stablization for 2D barcode in handheld devices
http://dl.acm.org/citation.cfm?id=1291233.1291394
Thursday, February 9, 2012
Training and Testing of Recommender Systems on Data Missing Not at Random
This paper talks about how users rate only a small portion of available movies. Normally recommender systems tend to ignore the missing ratings and treating all of them with the same weight. Some systems even try to correct the ratings or guess what they should be.
This paper tells how the missing ratings are not random and have meaning that should be taken into effect. They state that people only rate the movies that they like or know. It is very unlikely for someone to rate a movie they have never even heard of before. They use the example that fans of horror movies rate horror films and fans of romance movies rate romance movies. It is very unlikely for fans of either side to rate something from the other side unless they were fans of both. The missing ratings are not random and have just as much meaning as the traditional ratings. This approach helps users create a better recommender system.
Thanks and Gig 'em,
-Devin
The above blog was written about:
Training and testing of recommender systems on data missing not at random
http://dl.acm.org/citation.cfm?id=1835804.1835895
This paper tells how the missing ratings are not random and have meaning that should be taken into effect. They state that people only rate the movies that they like or know. It is very unlikely for someone to rate a movie they have never even heard of before. They use the example that fans of horror movies rate horror films and fans of romance movies rate romance movies. It is very unlikely for fans of either side to rate something from the other side unless they were fans of both. The missing ratings are not random and have just as much meaning as the traditional ratings. This approach helps users create a better recommender system.
Thanks and Gig 'em,
-Devin
The above blog was written about:
Training and testing of recommender systems on data missing not at random
http://dl.acm.org/citation.cfm?id=1835804.1835895
Tuesday, February 7, 2012
Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
This paper uses a few different methods in order to recommend a movie to the user, but the one that stands out most is the mood specific option. The team treats all movies as similar weights even if the rating is low. They use factors such as time of day, and other options in order to rate the similarity between the mood of movies and try to improve the recommendation.
This is a new model for the context aware recommendation system. They also propose using a matrix factorization model which is less computationally expensive than other methods. This allows them to compare a large data set quicker than others. This fact was learned during the Netflix Challenge. The research in this paper takes place during the Movieplot Challenge.
The reason that they do not rely solely on user ratings is because only a few movies actually receive high ratings from a large set of users and those movies would be recommended all the time. They try to use multiple inputs in order to select a movie based on the mood and interests of a user. They go on to show the algorithms they used to compute the various data and along with the sudocode. They then evaluate the results using Mean Average Precision (MAP) and Area Under Curve (AUC).
The above blog was written about the following paper, it can only be accessed while on the Texas A&M campus:
Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
http://dl.acm.org/citation.cfm?id=1869658
This is a new model for the context aware recommendation system. They also propose using a matrix factorization model which is less computationally expensive than other methods. This allows them to compare a large data set quicker than others. This fact was learned during the Netflix Challenge. The research in this paper takes place during the Movieplot Challenge.
The reason that they do not rely solely on user ratings is because only a few movies actually receive high ratings from a large set of users and those movies would be recommended all the time. They try to use multiple inputs in order to select a movie based on the mood and interests of a user. They go on to show the algorithms they used to compute the various data and along with the sudocode. They then evaluate the results using Mean Average Precision (MAP) and Area Under Curve (AUC).
The above blog was written about the following paper, it can only be accessed while on the Texas A&M campus:
Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
http://dl.acm.org/citation.cfm?id=1869658
Thursday, February 2, 2012
Content Aware Movie Recommendation
Most recommendation systems do not take in different variables such as the persons mood, the time of day, location, or the weather. Most just use a system that figures out if a person will like something or not. For example, I like the movie Gladiator, but I do not want to watch it all the time. I have to be in the right mood in order to want to watch that movie. This paper explains how to create a content aware recommendation system.
Some of the most popular and best systems use collaborative filtering. This system uses ratings from a large set of users to predict if you would like it or not. However, it fails to take account of age, gender, and education. It mainly uses the similarity between genres, actors, and directors to see if people would like this movie. This paper goes into calculating the users location so it can be added in to the recommendation system. Users in the same geographical area generally have more similar tastes than someone halfway across the world. Most movie recommendation systems also do not take into account multiple users on one account. It is becoming more common that a family will have an iTunes or Netflix subscription and more than one person will watch a movie. They usually watch it for different reasons and more than likely they have different tastes. So the recommendations not apply to every user.
In conclusion, this paper talked about context aware movie recommendation systems. Machine learning is a way to improve the system. It also comes as no surprise that adding more input to the system will create a better system, such as location, time of day, etc. Even with these new data fields, it doesn't necessarily mean a better system. The difficult part will be how to integrate the new fields to create better recommendations.
This blog was written about the paper listed below:
Context-aware Movie Recommendation based on Signal
Processing and Machine Learning
http://dl.acm.org/citation.cfm?id=2096112.2096114
Some of the most popular and best systems use collaborative filtering. This system uses ratings from a large set of users to predict if you would like it or not. However, it fails to take account of age, gender, and education. It mainly uses the similarity between genres, actors, and directors to see if people would like this movie. This paper goes into calculating the users location so it can be added in to the recommendation system. Users in the same geographical area generally have more similar tastes than someone halfway across the world. Most movie recommendation systems also do not take into account multiple users on one account. It is becoming more common that a family will have an iTunes or Netflix subscription and more than one person will watch a movie. They usually watch it for different reasons and more than likely they have different tastes. So the recommendations not apply to every user.
In conclusion, this paper talked about context aware movie recommendation systems. Machine learning is a way to improve the system. It also comes as no surprise that adding more input to the system will create a better system, such as location, time of day, etc. Even with these new data fields, it doesn't necessarily mean a better system. The difficult part will be how to integrate the new fields to create better recommendations.
This blog was written about the paper listed below:
Context-aware Movie Recommendation based on Signal
Processing and Machine Learning
http://dl.acm.org/citation.cfm?id=2096112.2096114
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