Content-Based Decision Making Essay

Movies have the ability to transport people to different times and places and distract them from ordinary everyday reality. They allow for a range of emotions to be experienced. At their core, movies examine the human condition. There are plenty of deeper truths woven into screenplays and plenty of lessons to be learned, even when an individual is solely seeking entertainment.

Another thing I look for in movies is actors who are good at acting. In my opinion, I believe that a bad actor can ruin the whole movie. Sometimes it can

An overall review score is already assigned by the user when he/she writes the reviews. From these, the proposed probabilistic model calculates the aspect score for each aspect from the user's list of preferences and presents those reviews that has the best aspect score from different reviews. The model implicitly predicts the user aspects and their scores from the review provided by the user and then mines for the reviews that match the aspect score similar to those of the

Now our service is to acquire the user who has the most likelihood to be recommended. First we use the probability function $P(r,u)$ using the communication between the user and recommender. If the probability is higher than our defined threshold value then grouped the user. All the nodes in this group are our expected nodes. Now for the nearest one we run BFS and extract the best one for recommendation.

With the large variety of interests that flourish within the society, it is no shock that genres exist, especially in film, as the definition of entertainment differs from person to person. A genre is a category of films that are characterised by similarities in the narrative elements and conventions found within the films. Genres are extremely useful to both film producers and audiences as it helps them narrow down the target audience and search for films that are in their range of interests. No one wants to watch a political drama overrun with teenagers or go into a cinema with the intentions of watching a romantic comedy, only to receive blood and gore instead.

Most people watch movies. They might all watch different genres. Some people like to watch scary movies and some people don’t. It depends on the person. But if you’re like me then you like to watch different kinds of movies, it depends on the mood that you’re in. But when I’m watching movies I like to have this one special actor and his name is Matthew McConaughey.

Films should be both entertainment and should also tackle challenging ideas. It’s good to have varieties of films, because we watch film depending on what we are in the mood for. Sometimes we watch to learn about history we never knew about and

According to Reel News Daily, on average, there are 57 movies released every month or 684 movies each year. That’s way more movies than the average person watches in a given year. So, how do we decide what movies to go watch and what not to watch? What’s pleasing and what’s appalling? Fortunately, we have critics who watch most of these movies and write reviews to help narrow down what movies to and not to view. One of these critics is Robert Ebert. Robert Ebert is an honorable critic to base judgements off of because he does an exceptional job summarizing the plot, stating his personal opinions, and speaking about the actors/director’s roles in the film.

• You will find the list of films you can choose from in the assignment section of the course.

There are three major standards through which I decide whether or not I will watch a film: reputation, and genre. Reputation is inclusive of friends and ratings of the film. Usually my friends and I have similar tastes and we are aroused or repulsed by the same films. Reputation is a strong and stable standard for deciding which film to go to, because with such a large population of movie watchers, major biases don't affect the reputation and ratings are fair and accurate. Also, people are inclined to give high ratings to movies that touch them or really make them think regardless

The movies under this category have attracted the attention of many reviewers because of some reasons. The films are widely appreciated because of their wider exposition of culture as well as sophisticated and rich subject matter. The show of culture and emotional expression that the movie contains has drastically changed the description of the category from action films to relationship film. Additionally, the movies

In addition, an independent research conducted in Bryant University suggest that out of 50 domestic students, 35 students like watching Indian movies whereas 8 did not like Bollywood movies and 7 have not watched it yet. Though the sample size is small, it serves to show that the domestic people are watching and accepting Bollywood movies. Netflix is an American internet based steaming media provider company which provides more than 100,000 movies. The most interesting fact is that 89% of the Foreign Movie section in Netflix, is Bollywood movies. Netflix has about 33.1 million viewers in United States alone and yet the “Most Watched Movie “and “Foreign Movie” section consist of many Bollywood movies. This shows that Bollywood has influenced the domestic audience as well the industry itself. The influence of Bollywood element and the Indian culture can be seen quite evidently in the music, dance and visual representation in Hollywood movies.

Let?s define what is Group Decision Making, decisions made by committees, think tanks, teams and groups. They may include borrowers, lenders, producers, buyers, scientists and other experts, environmentalists, and real estate developers and so on.

In this paper, we present a survey of applications implementing collaborative filtering methods, content-based filtering methods and deep neural networks with Restricted Boltzmann Machines (RBMs) for generating recommendations. The drawbacks of collaborative and content-based methods is analyzed and proof of concept is provided for the need of deep learning based recommender systems. We introduce RBM algorithm and its applications for use in generic recommendation generation and propose to implement a deep learning neural networks algorithms(RBM) to create

Many movies or stories are fit into a specific category. For example, there is comedy,