I am implementing a number of my ideas which are about providing big data processing and machine learning services mainly using HTTP. I just finished another one.
This is an introduction to a service which helps business owners to find out what is the taste of their websites visitors or customers. Moreover the service recommends visitors based on a machine learning technique which has been called Collaborative Filtering. I’ve called it “Next”.
“Item” is whatever the website provides for visitors such as a web content, a product in the products page of a company, an advertisement or anything else the business owner provides for the visitors.
“Visitor” is an anonymous or authenticated user which surfs an online-shop, a news website, a technical blog a real-state website or whatever else. Visitor always is a customer or could be potentially. So we think she needs to be recommended by proper messages are interesting for her.
A “Recommender Engine” is what I developed based on a number of well-known techniques and open-source products which learn what the visitors do with the items. Then it recommends visitors the other items they might be interested to visit (read, see, or buy).
“Recommendation” is a message contains a list of items provided for the current visitor based on all users interactions.
Who Already Uses The Same Technology?
- Recommend additional books
- Frequently bought together books
- Plays music with similar characteristics
- Content based filtering based on properties of song/artist
- Based also on user’s feedback
- Recommends songs by observing the tracks played by user and comparing to behavior of other users
- Suggests songs played by users with similar interests
- Collaborative filtering based on user’s previous ratings and watching behaviors (compared to other users)
- Recommends users during searching through questions and answers.
Many other businesses use this technique as well.
If you need what the mentioned companies do to recommend their users, and you don’t have such a team to develop an instance for your website then my solution would be interesting for you.
The recommender service provider doesn’t need to know anything about the visitors which surfing the website. Instead it just needs to mark each visitor by a unique id. It also doesn’t need to know anything about the item as well. A book, a mobile phone or a posted article, they all would be the same in the way it learns. A unique id (The URL) would be enough for recognizing each item at all.
By using this service, rating and comparing items (from the point of visitors view) would be available.
The following questions would be answered on demand:
- Which items would be interesting for a certain visitor?
- Which users might like an item more than others?
- How much two certain items are close together (By the visitor’s point of view)?
- Who would like to know about a certain new item?
How To Use It?
Who Can Be Served?
The solution is pretty scalable and blazing fast. It means growing in number of visitors, items and incoming messages all would be handled by adding more machines to the cluster.
It is Growing Up
I am adding more administrative features such as Big Data Visualization and Content Based Recommendations algorithms while it already serves a candidate client.