Watching movies is one of the social activities typically done in groups. Emotion is the most vital factor that affects movie viewers' preferences. So, the emotional aspect of the movie needs to be determined and analyzed for further recommendations. It can be challenging to choose a movie that appeals to the emotions of a diverse group. Reaching an agreement for a group can be difficult due to the various genres and choices. This paper proposes a novel approach to group movie suggestions by examining emotions from three different channels: movie descriptions (text), soundtracks (audio), and posters (image). We employ the Jaccard similarity index to match each participant's emotional preferences to prospective movie choices, followed by a fuzzy inference technique to determine group consensus. We use a weighted integration process for the fusion of emotion scores from diverse data types. Then, group movie recommendation is based on prevailing emotions and viewers' best-loved movies. After determining the recommendations, the group's consensus level is calculated using a fuzzy inference system, taking participants' feedback as input. Participants (n=130) in the survey were provided with different emotion categories and asked to select the emotions best suited for particular movies (n=12). Comparison results between predicted and actual scores demonstrate the efficiency of using emotion detection for this problem (Jaccard similarity index = 0.76). We explored the relationship between induced emotions and movie popularity as an additional experiment, analyzing emotion distribution in 100 popular movies from the TMDB database. Such systems can potentially improve the accuracy of movie recommendation systems and achieve a high level of consensus among participants with diverse preferences.

Collaborative filtering is a clever recommendation system technique that predicts what you'll like based on others with similar tastes. Super useful for businesses like Netflix or Amazon to personalize suggestions! For example, if you and another user both love sci-fi, it'll recommend shows they enjoyed, knowing you'll probably like them as well. Learning latent factors is all about discovering hidden features that help explain user-item interactions, like why people like certain movies. For example, suppose we're recommending movies.

We are calculating the number of ratings using the count method of a data frame. Using the count method helps count the number of not empty values for each column and returns the result for each column. Sorting by number of ratings, we now see some results. "Star Wars," which is a very famous movie, has got a mean of 4.35 as a rating from 583 users. We are creating a pivot table just to quickly summarize the amount of data we have.

The aim of this project is to recommend the movies to the user based on their favorite movies. Like, whenever a user searches for any movie so this system recommends movies that are of the same type. So, let's look up the code!! The shape of this dataset is (4803,24). After this, I selected some relevant features that are essential.

Machine learning is one of the important areas of AI. It plays an important role in identifying the trends and behavior of a mass of people using a given dataset. Aces like Google, Facebook, Uber, and many other leading companies use machine learning as the backbone of their operations. Overall, machine learning is a highly sought after skill these days. The more demand for this domain and its use, the more intimidating it becomes for newbies to learn.

Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors. In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a set of data to discover potentially predictive relationships.

Most of the Machine Learning, Deep Learning, Computer Vision, NLP job positions, or in general every Artificial Intelligence (AI) job position requires you to have at least a bachelor's degree in Computer Science, Electrical Engineering, or some similar field. If your degree comes from some of the world's best universities than your chances might be higher in beating the competition on your job interview. But looking realistically, not most of the people can afford to go to the top universities in the world simply because not most of us are geniuses and don't have thousands of dollars, or come from some poor country (like we do). No with the high demand of skilled professionals from these fields, there are exceptions being made, so we can see that people who don't come from these fields, are learning and adjusting themselves in order to get that paycheck. In this article, we are going to list some of the free Artificial Intelligence courses that come from Harvard University, MIT University, and Stanford University that anyone can attend, no matter where they live.

With the continuous development of network technology and the ever-expanding scale of e-commerce, the number and variety of goods grow rapidly and users need to spend a lot of time to find the goods they want to buy. To solve this problem, the recommendation system came into being. The recommendation system is a subset of the Information Filtering System, which can be used in a range of areas such as movies, music, e-commerce, and Feed stream recommendations. The recommendation system discovers the user's personalized needs and interests by analyzing and mining user behaviors and recommends information or products that may be of interest to the user. Unlike search engines, recommendation systems do not require users to accurately describe their needs but model their historical behavior to proactively provide information that meets user interests and needs.

We apply the Artificial Immune System (AIS) technology to the Collaborative Filtering (CF) technology when we build the movie recommendation system. Two different affinity measure algorithms of AIS, Kendall tau and Weighted Kappa, are used to calculate the correlation coefficients for this movie recommendation system. From the testing we think that Weighted Kappa is more suitable than Kendall tau for movie problems.