Having implemented an algorithm to recommend products with some success, I'm now looking at ways to calculate the initial input data for this algorithm.
My objective is to calculate a score for each product that a user has some sort of history with.
The data I am currently collecting:
All of this data is timestamped.
There are a couple of things I'm looking for suggestions on, and ideally this question should be treated more for discussion rather than aiming for a single 'right' answer.
Just to avoid this question being derailed with the wrong kind of answers, here is what I'm doing once I have this data for each user:
Basically, I'm not looking for ideas on what to do once I have the input data (I may need further help with that later, but it's not the point of this question), just for ideas on how to generate this input data in the first place
Collaborative filtering is a technique that can filter out items that a user might like on the basis of reactions by similar users. It works by searching a large group of people and finding a smaller set of users with tastes similar to a particular user.
Which technique is proper for solving collaborative filtering problem? The standard method of Collaborative Filtering is known as Nearest Neighborhood algorithm.
Content-based filtering uses machine learning algorithms to predict and recommend new, yet similar, items to users. It uses item features to group similar items together. Collaborative filtering solely uses past interactions between the customers and the products they've used to recommend new items.
Collaborative filtering is used by most recommendation systems to find similar patterns or information of the users, this technique can filter out items that users like on the basis of the ratings or reactions by similar users.
Here's a haymaker of a response:
What kind of products are you selling? That might help us answer you better. (Since this is an old question, I am addressing both @Andrew Ingram and anyone else who has the same question and found this thread through search.)
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