Content based filtering is one of the most common recommending approaches. It provides recommendation based on items user currently likes or uses. For example, if a user likes the movie Frozen, content based filtering will find movies similar to Frozen according to movie characteristics such as movie category, producers, actors and movie length etc.
“The steps in recommending products or contents to the user in content based filtering are as follows:
Identify the factors which describe and differentiate the products and the factors which might be influential in weather a user would buy the product or not,
Represent all the products in terms of those factors or descriptors or attributes,
Create a tuple or number vector for each product that represents the strength of each factors for the product,
Now start to look at the users and their history and create a user profile based on their history. It will have the same number of factors and their strength would indicate how much influenced the user is towards that factor,
Recommend the user those products that are nearest to them in terms of those factors.”
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My name is Thomas Khoo. I started Applied Analytics with Nick not only for his reasons stated before but also because of the power data has to bridge things together. After reading the articles posted on our Facebook page, it is clear that data plays an underlying role in many things. And the best thing: anyone can learn it.
The resources to learn visualization software, like Tableau and Power BI, and programming languages such as Python and SQL, are available at our website (aacuw.org). Under the Resources tab you’ll find links to languages and programs that can teach you how to use them. Applied Analytics provides the opportunity for individuals to enable themselves with the data analytics and machine learning skills employers are seeking.
You may ask at this point, what does that have to do with community? The community aspects are derived from the accessibility and availability. There are no financial barriers to consider, no discrimination or bias, no ambiguity about what you know. You have the skills they want. Period. Not many disciplines bear all three.
On top of the individuals benefit, many analytics require the specific understanding of the data’s nature. Data takes on any form. Data can predict how a protein will develop without having to wait months while on the other side data can predict when a woman is pregnant. Data give you incredible power. Understanding data’s cohesive nature will create synergy that will elevate everyone to higher levels and brighter skies.
“It is a capital mistake to theorize before one has data.”
Fictional character quote aside, data is utterly important, both then and now. Those with the data and the knowledge to interpret it have a significant advantage over those without. Today’s top companies, top executives, and top job candidates understand that a current data aptitude is required to excel today and to survive in the future.
This blog is an extension of the content curated and created by the Applied Analytics Club at UW (AACUW). AACUW was founded in the fall of 2018 to offer a broad exposure of data science and analytics to the University of Washington community. The purpose of this blog is to extend our mission further into the online space to make data education even more accessible. We will post original content including step-by-step tutorials, club updates, and opinion posts discussing data from university students’ lenses. Finally, we will re-post significant content from our other online outlets such as our YouTube page, Linkedin page, Instagram page, and Facebook page (all content including event details are posted on FB… we recommend you follow/like us).
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