Have you heard about the famous Jam Experiment? In 2000, psychologists Sheena Iyengar and Mark Lepper from Columbia and Stanford University presented a study based on their field experiment. On a regular day, consumers shopping at an upscale grocery store at a local food market were presented with a tasting booth which displayed 24 varieties of Jam. On some other day, the same booth displayed only 6 varieties of Jam. The experiment was being conducted to adjudge which booth would garner more sales and it was assumed that more varieties of jam would fetch more people to the counter thereby getting more business.
So, let us now preprocess our data! Natural Language Processing techniques are our savior when we have to deal with textual data. Since our data cannot be fed to any machine-learning model unless we clean it, that's where NLP comes to play! Let's clean our text data – Firstly, let us create a new column in our dataframe that will hold all necessary keywords required for the model.
Recommender systems are increasingly used for personalised navigation through large amounts of information, especially in the e-commerce domain for product purchase advice. Whilst much research effort is spent on developing recommenders further, there is little to no effort spent on analysing the impact of them - neither on the supply (company) nor demand (consumer) side. In this paper, we investigate the diversity impact of a movie recommender. We define diversity for different parts of the domain and measure it in different ways. The novelty of our work is the usage of real rating data (from Netflix) and a recommender system for investigating the (hypothetical) effects of various configurations of the system and users' choice models.We consider a number of different scenarios (which differ in the agent's choice model), run very extensive simulations, analyse various measurements regarding experimental validation and diversity, and report on selected findings. The choice models are an essential part of our work, since these can be influenced by the owner of the recommender once deployed.
A recommendation system generates a compiled list of items in which a user might be interested, in the reciprocity of their current selection of item(s). It expands users' suggestions without any disturbance or monotony, and it does not recommend items that the user already knows. For instance, the Netflix recommendation system offers recommendations by matching and searching similar users' habits and suggesting movies that share characteristics with films that users have rated highly. In this tutorial, we will dive into building a recommendation system for Netflix. This tutorial's code is available on Github and its full implementation as well on Google Colab.