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Building a Simple Recommender System in Google Spreadsheet for Desklib

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Added on  2019-10-09

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This article explains how to build a simple recommender system in Google Spreadsheet for Desklib, an online library for study material with solved assignments, essays, dissertation etc. It also covers basic data manipulation using spreadsheet's formula. The article discusses the importance of recommender systems and how they work. It also provides tasks to complete using spreadsheet formulas and answer relevant questions. The article provides a dataset of 291 popular feature films produced from 1969 to 2008. The article also provides hints and a marking scheme for the tasks. The article concludes with submission guidelines and resources for further reading.

Building a Simple Recommender System in Google Spreadsheet for Desklib

   Added on 2019-10-09

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AimsIn this phase you will:carry out basic data manipulation using spreadsheet's formulaimplement a simple recommender system in spreadsheetIntroductionIn 2014, Hollywood has releasedmore than 600 movies: that is, one movie every day, plus 4 more during the weekend for you to watch. In the last decade, the way consumers access such a huge collection of movies has changed. With the proliferation of the high speed Internet, online movie rental (either via DVD or streaming) companies are killing the physical video rental shops.Launched in 1999,Netflix--the largest US online movie rental then--announced itsfirst billionth DVD deliveryin February 2007. It claimed to spend about $300 million a year on postage alone. Around 2008, Australia'sBigPond MoviesandQuickFlixhad thousands of DVDs on offer only a mouse click away. Subsequently, in 2009,Netflixdelivered its 2 billionth delivery in BluRay. Recently, inMarch 2015,Netflixofficially launched its streaming service in Australia, competing with existing providers likeStanandPresto.Parallel to this trend, consumers--professional critics or moviegoers---have been sharing and exchanging information about movies online. TheIMDb, the largest online movie database, stores information about movies, actors, directors, and any other information you can think of. There are many other similar sites that provide a comprehensive collection of reviews and critics:metacritic,rottentomatoes,Yahoo! Movies, andmovie review query engine. All of these sites allow people to collaboratively discuss and rate their favourite movies online.One important function of these sites is to help people to select movies they like by looking at lists of movie reviews from around the world. This is a case of information filtering.Online recommendation systemsare becoming important information filtering tools as we are overwhelmed by digital content.Pandora's music recommendation system andAmazon'sbook recommendationare such examples. These systems are very useful, not only for the audience to find their way through millions of options, but also for business to up-sell their products (Do you want to upsize your Big Mac meal?). It is so important thatNetflix offers USD$1,000,000to anyone who can improve their movie recommendation engine.Recommender systemA 'recommender system' presents a list of items (books, movies, music) that are likely to be of interest to a user, based on what it knows about that user and the items. It makes use of intrinsic properties of the large collection of items (the content-based approach), the user's social environment (the collaborative filtering approach),or a combination of both. There are many ways to predict what a person would like, but there is no one correct way - as billions of dollars spent on marketing will attest to.
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In a 'movie recommender' system, for example, a content-based approach may employ information such as actors, directors and/or movie genre. The combination ofwhat the audience thinks about the movies and the audience profiles can be utilized in the collaborative filtering approach (two users with the same profile are more likelyto enjoy the same movies). As people's borrowing/consuming habits get recorded, the amount of data that can be used in the system only increases.In this assignment, you will build a simple version of such a system, which uses information about movies to find similar movies and produce recommendations.TasksData SetsThe given data set contains information about 291 popular feature films produced from 1969 to 2008. The data set captures data such as the movie name, censorship rating, genre, director, actors, score from various critics, and worldwide gross.(Attached)Part 1 - Basic Task Using spreadsheet formulas, complete the following tasks and answer all the relevant questions.1.Compare the performance among movie genres based on the worldwide gross of the movies with the same genre. Ignore genres that have less than 5 movies. Visualise the comparison using appropriate chart type. Which three genres are the worst performers? Compare the performance of movie ratings (PG, G, etc) based on the same measure. Again, ignore ratings that have less than 5 movies. Visualise the comparison again. Do PG-rated movies generally earn better than R-rated movie?2.Which three of the given reviewers in the movie data (Washington Post, Chicago Sun-Times, The New York Times, LA Weekly, Los Angeles Times, Rolling Stone, Wall Street Journal, Entertainment Weekly, Empire, Variety, Salon.com, The Onion (A.V. Club), TV Guide, Slate) are the most consistent with the 'metascore'? You can do this by calculating the average gaps between the metascore value and the score from a particular reviewer. Visualise the average gaps of all reviews to see how close they are to metascore. Consider 0 as an empty score. State your assumption when dealing with missing data.3.Present a table of actors versus genres to show the number of movies in eachgenre that a particular actor is featured in. Show only actors which have appeared in at least 6 movies. Colour the cells that contain these counts so that higher counts can be distinguished from lower counts. Include as the last column the total number of movies the actor is featured in. Correspondingly, include as the last row the total number of movies within each genre. Present the actor names in descending order (based on the movie count)....genre......
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