In our last post, we discussed some machine learning examples that any user encounters knowingly or unknowingly. This post presents applications which use a combination of machine learning, artificial intelligence, speech recognition, image recognition, natural language processing, and many other interesting technologies.
(Products on Amazon, Movies, Shows on Netflix, Videos on Youtube, Facebook News Feed, Google Ads.)
A product recommendation is primarily a filtering system that attempts to predict and show the items that a user would like to buy. It may not be completely accurate, but if it displays you what you like then it is doing its function right. Recommender systems have become increasingly popular in recent years, and are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. Recommender Systems enhances user experience and provide more exposure to larger inventory. eg: Products on Amazon, Movies, Shows on Netflix, Videos on Youtube, Music on Spotify, Facebook News Feed, Google Ads.
There are several types of product recommendation systems, each based on different machine learning algorithms which are used to conduct the data filtering process. The main categories are content-based filtering (CBF), collaborative filtering (CF), complementary filtering, and hybrid recommendation systems, which use a combination of CBF and CF.
Content-based filtering: CBF tracks a user’s actions, such as products bought or clicked on, web pages viewed, time spent browsing various product categories, etc. It then uses this information to create a customer profile. This profile is then compared to the product catalog to make recommendations.
Collaborative filtering: This filtering method is usually based on collecting and analyzing information on user’s behaviors, their activities or preferences and predicting what they will like based on the similarity with other users.
Virtual Personal Assistants:
(Amazon Alexa, Apple’s Siri, Google Now and Microsoft’s Cortana)
Virtual Personal Assistants (VPA) are software program meant to interact with an end user in a natural way, to answer questions, follow a conversation and accomplish different tasks
Such tasks, historically performed by a personal assistant or secretary, include taking dictation, reading text or email messages aloud, looking up phone numbers, scheduling, placing phone calls and reminding the end user about appointments. Popular virtual assistants currently include Amazon Alexa, Apple’s Siri, Google Now and Microsoft’s Cortana — the digital assistant built into Windows Phone 8.1 and Windows 10.
The technologies that power virtual assistants require massive amounts of data, which feeds artificial intelligence (AI) platforms, including machine learning, natural language processing, and speech recognition platforms. As the end user interacts with a virtual assistant, the AI programming uses sophisticated algorithms to learn from data input and become better at predicting the end user’s needs. The accuracy, speed and contextual abilities of Alexa, Google Assistant and Siri are all thanks to machine-learning algorithms and massive servers, owned by their respective parent companies (Apple, Amazon, and Google).