Recommendations Personal vs Statistical

In today’s world, every customer is faced with multiple choices. For example, If I’m looking for a book to read without any specific idea of what I want, there’s a wide range of possibilities how my search might pan out. I might waste a lot of time browsing around on the internet and trawling through various sites hoping to strike gold. I might look for recommendations from other people.

But if there was a site or app which could recommend me books based on what I have read previously, that would be a massive help. Instead of wasting time on various sites, I could just log in and voila! 10 recommended books tailored to my taste.

“Recommendation” carries the implication of authority. Not in a “I’m your boss or a cop” way, but in a “I know what I’m talking about” way. Probably 95% of its use is in recommending restaurants, attractions, events, hotels, stores, books, movies, electronic devices, etc. etc. etc. When Amazon or Spotify says “you might also like these things…..” that is a recommendation engine generating those things. It’s called that for a reason. You can absolutely use it outside that context, but as with anytime you use a word outside of its typical context, you can convey information with that choice. For example, if you make a suggestion about a personal issue, you’re conveying that you are a bystander trying to help. But if you make a recommendation, you’re implying that you know what you’re talking about, which can be construed as arrogance, contempt of the person for not figuring out the right answer themselves, or merely taking a more active interest in the situation. Or it could mean nothing. Tone of voice and body language can certainly override most of these nuances, But be aware anyway.
Steven Brown on Quora

A recommendation engine filters the data using different algorithms and recommends the most relevant items to users. It first captures the past behavior of a customer and based on that, recommends products which the users might be likely to buy.

The content-based filtering algorithm finds the cosine of the angle between the profile vector and item vector, i.e. cosine similarity. Suppose A is the profile vector and B is the item vector, then the similarity between them can then be calculated. Based on the cosine value, which ranges between -1 to 1, the movies are arranged in descending order and one of the two approaches is used for recommendations Top-n approach or Rating scale approach.

The User-User collaborative filtering algorithm first finds the similarity score between users. Based on this similarity score, it then picks out the most similar users and recommends products which these similar users have liked or bought previously. In terms of our movies example from earlier, this algorithm finds the similarity between each user based on the ratings they have previously given to different movies. The prediction of an item for a user u is calculated by computing the weighted sum of the user ratings given by other users to an item i.