A Recommendation systems have impacted or even redefined our lives in many ways. It works in well-defined, logical phases which are data collection, ratings, and filtering.
Recommendation systems have impacted or even redefined our lives in many ways. One example of this impact is how our online shopping experience is being redefined. As we browse through products, the Recommendation system offer recommendations of products we might be interested in. Regardless of the perspective — business or consumer, Recommendation systems have been immensely beneficial. And big data is the driving force behind Recommendation systems. A typical Recommendation system cannot do its job without sufficient data and big data supplies plenty of user data such as past purchases, browsing history, and feedback for the Recommendation systems to provide relevant and effective recommendations. In a nutshell, even the most advanced Recommenders cannot be effective without big data.
A Recommendation system works in well-defined, logical phases which are data collection, ratings, and filtering. These phases are described below.
Let us assume that a user of Amazon website is browsing books and reading the details. Each time the reader clicks on a link, an event such as an Ajax event could be fired. The event type could vary depending on the technology used. The event then could make an entry into a database which usually is a NoSQL database. The entry is technical in content but in layman’s language could read something like “User A clicked Product Z details once”. That is how user details get captured and stored for future recommendations.
How does the Recommendation system capture the details? If the user has logged in, then the details are extracted either from an http session or from the system cookies.
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