Recommender System Using Collaborative Filtering Algorithm

With the vast amount of data that the world has nowadays, institutions are looking for more and more accurate ways of using this data. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. However, to reach this goal, systems need to parse a lot of data and collect information, sometimes from different resources, and predict how the user will like the product or item. The computation power needed is considerable. Also, companies try to avoid flooding customer mailboxes with hundreds of products each morning, thus they are looking for one email or text that will make the customer look and act. The motivation to do the project comes from my eagerness to learn website design and get a deep understanding of recommender systems. Applying machine learning dynamically is one of the goals that I set for myself and I wanted to go beyond that and verify my result. Thus, I had to use a large dataset to test the algorithm and compare each technique in terms of error rate. My experience with applying collaborative filtering helps me to understand that finding a solution is not enough, but to strive for a fast and ultimate one. In my case, testing my algorithm in a large data set required me to refine the coding strategy of the algorithm many times to speed the process. In this project, I have designed a website that uses different techniques for recommendations. User-based, Item-based, and Model-based approaches of collaborative filtering are what I have used. Every technique has its way of predicting the user rating for a new item based on existing users’ data. To evaluate each method, I used Movie Lens, an external data set of users, items, and ratings, and calculated the error rate using Mean Absolute Error Rate (MAE) and Root Mean Squared Error (RMSE). Finally, each method has its strengths and weaknesses that relate to the domain in which I am applying these methods

Wisdom of the Crowd Vs Reviews of the Experts: A Case Study Regarding Predicting Movie Box-Office Results

Teadlased on aastakümneid tegelenud filmide kassatulu ennustamisega, sest iga aasta linastub suur hulk teoseid, mille tulemused üllatavad nende rahastajaid kas heal või halval viisil, sõltuvalt esialgsetest prognoosidest. Eelnevad uurimustööd on avaldanud vastakaid tulemusi filmikriitikute arvustuste kasutamise kohta filmide kassatulu ennustamiseks. Niisamuti on kaasatud sotsiaalmeedia ühe võimaliku andmeallikana filmide müügiedu prognoosimiseks. Käesolevas töös uuritakse, milline neist kahest erinäolisest allikast on kasulikum ennustamaks parema täpsusega filmide kasumlikkust. Uuritavateks andmeteks oleme kogunud viimase kolme aasta jooksul linastunud Hollywoodi ja Bollywoodi filmid, mis on erineva geograafilise asukoha ning kultuurilise taustaga. Kollektiivse tarkuse näitena uurime sotsiaalvõrgustiku Twitteri andmeid ning võrdleme neid filmikriitikute arvustustega Hollywoodi ning Bollywoodi filmiportaalidest Metacritic ja SahiNahi. Kaasame mitmeid erinevaid tunnuseid ning rakendame erinevaid masinõppe algoritme ennustusmudelite ehitamiseks. Meie vaatluste tulemused näitavad, et võrreldes filmikriitikute eksperthinnangutega pole kollektiivsete teadmiste abil võimalik filmide kassatulu paremini ennustada ega vastupidi.Predicting movie sales figures has been a topic of interest for research for decades since every year there are dozens of movies which surprise investors either in a good or bad way depending on how well the film performs at the box-office compared to the initial expectations. There have been past studies reporting mixed results on using movie critics reviews as one of the sources of information for predicting the movie box-office outcomes. Similarly using social media as a predictor of movie success has been a popular research topic. In this thesis, we perform a case study to evaluate out of two – the (wisdom of the) crowd or the movie critics reviews, which one can predict the outcome of the movies more accurately. We analyze the Hollywood and Bollywood movies from the last three years, which belong to two different geo as well as cultural locations. We used Twitter for collecting the wisdom of the crowd and used movie critics review scores from movie review aggregator sites Metacritic and SahiNahi for Hollywood and Bollywood movies respectively. To perform our evaluation, we extracted various features and used them to build prediction models using different machine learning algorithms. After measuring the performance of prediction models using features from both Twitter and movie critic reviews, we did not find conclusive evidence to declare a clear-cut winner

Turning Followers into Dollars: The Impact of Social Media on a Movie’s Financial Performance

This paper examines the impact of social media, specifically Twitter, on the domestic gross box office revenue of 207 films released in the United States between 2009 and 2011. We find that under two different specifications the impact of Twitter on gross revenue and gross revenue per theater is statistically significant when accounting for several control variables. The models show statistical significance of runtime, and production budget. We also find that a film’s release period, genre, rating received, and whether or not it is based on previous material proved to be statistically significant factors in determining a film\u27s domestic gross

Predicting Gross Revenue Using Online Movie Reviews

Today, many people check the ratings and reviews of a movie before they watch it. A review can be easily published online and seen by thousands of people, and this can change a person’s opinion on whether or not they see the film. With the increasing presence of online platforms, it has changed the way people express their thoughts and feelings. There are many different platforms people can go to find varying opinions on a particular movie. This research will consider the problem of predicting a movie’s overall gross revenue. We focus on ratings, reviews and information given on the IMDb Website. (https://www.imdb.com) A list of 4,265 movies and their corresponding reviews were collected between January 2005 and December 2019. In our research, we use RStudio and sentiment analysis to determine a review’s emotions and opinions. We investigate whether the sentiment in a movie’s reviews can predict overall gross revenue, as well as other predictors given on the IMDb website

Social-media monitoring for cold-start recommendations

Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem

21st Century Film Criticism: The Evolution of Film Criticism from Professional Intellectual Analysis to a Democratic Phenomenon

Film criticism has changed since its inception and will continue to change moving forward. The evolution of film criticism has largely been a story of the shift from an elite field of intellectual exploration by a few knowledgeable experts to a democratic phenomenon where expert analysis is aggregated and averaged, and the lines are blurred between true expertise and the random opinions of the masses. This paper will address the transition from the birth of film criticism to its popularization through the 90s, to what it has become today. By exploring the nature of film criticism historically and reviewing the key elements of its growth from Victorian times through its emergence as an established field in the 1930s, 40s and 50s and its heyday in the 60s and 70s, we can understand the context of its evolution. This will provide a perspective to view today’s approach to film criticism with a clearer eye and a thorough analysis of film criticism in the digital age. It will demonstrate that more is not always a good thing, and the democratization of film criticism has not necessarily been all good

Influence of social media on performance of movies

"May 2014."Thesis advisor: Dr. Wenjun Zeng.Includes bibliographical references (pages 51-53)

Using Consumer-Generated Social Media Posts to Improve Forecasts of Television Premiere Viewership: Extending Diffusion of Innovation Theory

Billions of US dollars in transactions occur each year between media companies and advertisers purchasing commercials on television shows to reach target demographics. This study investigates how consumer enthusiasm can be quantified (via social media posts) as an input to improve forecast models of television series premiere viewership beyond inputs that are typically used in the entertainment industry. Results support that Twitter activity (volume of tweets and retweets) is a driver of consumer viewership of unscripted programs (i.e., reality or competition shows). As such, incorporating electronic word of mouth (eWOM) into forecasting models improves accuracy for predictions of unscripted shows. Furthermore, trend analysis suggests it is possible to calculate a forecast as early as 14 days prior to the premiere date. This research also extends the Diffusion of Innovation theory and diffusion modeling by applying them in the television entertainment environment. Evidence was found supporting Rogers’s (2003) heterophilous communication, also referred to by Granovetter (1973) as “weak ties.” Further, despite a diffusion pattern that differs from other categories, entertainment consumption demonstrates evidence of a mass media (external) channel and an interpersonal eWOM (internal) channel