I'm already interested in recommendation systems when I started to use the website like Taobao, Weibo,Facebook,LinkedIn,Amazon...These popular websites adopt different method to realize recommendations.
Amazon uses recommendation systems to recommend new products to users.It wants to show us with products that we will be interested in seeing.Amazon's method of recommendation is kind of complex,but it is centered around the products a user views and purchases.They utilize a system known as item-to-item collaboration filtering.
Youtube utilizes recommendation systems to bring videos to us that we will be interested in. They are designed to increase the numbers of videos we will watch, increase the length of time we spend on the site, and maximize the enjoyment of our Youtube experience.When I watch the video on YouTube, I can't stop opening the next one in the recommendation list.
LinkedIn is a business-oriented social networking site, forms recommendations for people you might know, jobs you might like, groups you might want to follow, or companies you might be interested in. LinkedIn uses Apache Hadoop to build its specialized collaborative-filtering capabilities.
Amazon's Recommendations
Amazon uses recommendation systems to recommend new products to users.It wants to show us with products that we will be interested in seeing.Amazon's method of recommendation is kind of complex,but it is centered around the products a user views and purchases.They utilize a system known as item-to-item collaboration filtering.
Amazon's Item-to-Item Collaborative Filtering focuses on the items rather than the users. It does not try to match the user to similar customers. Instead, it matches each of the user's purchased and rated items to similar items, and then it merges the similar items among them into one recommendation list unique to the user.
YouTube's Recommendations
Youtube utilizes recommendation systems to bring videos to us that we will be interested in. They are designed to increase the numbers of videos we will watch, increase the length of time we spend on the site, and maximize the enjoyment of our Youtube experience.When I watch the video on YouTube, I can't stop opening the next one in the recommendation list.
In order to obtain personalized recommendations, Youtube's recommendation system combines the related videos association rules with the user's personal activity on the site. This includes several factors. There are the videos that were watched - along with a certain threshold, say by a certain date. After all, you don't want to count videos watched from 2 years ago if the user has watched enough videos, most likely. Also, Youtube factors in with emphasis any videos that were explicitly "liked", added to favorites, given a rating, added to a playlist. The union of these videos is known as the seed set.
LinkedIn's Recommendations
LinkedIn is a business-oriented social networking site, forms recommendations for people you might know, jobs you might like, groups you might want to follow, or companies you might be interested in. LinkedIn uses Apache Hadoop to build its specialized collaborative-filtering capabilities.
Hi, it is really inspiring that you listed some general ideas of what kind of recommendation systems the well known sites use. I am just wondering where did you get these kind of information, because I am also interested, could you send some to me , so I can have a further look. Many thanks.
回复删除Thank you for your comment.I got all these information from google.I can share these with you through WeiXin if you are interested.
删除Hi dear~ I'm totally attracted by your blog! It's really useful and you're very nice to share the different kinds of recommendation systems among four companies with us. Nowadays, some video platforms focus more on user expierence. Because the good recommendation system can increase the viscosity of users.
回复删除Thank you for your approval dear!The viscosity of users is one of the key point for user friendly products.Hope the recommendation system can become better and better so that we can enjoy better user experience.
删除I have not used LinkedIn. But I'm very interest in it . Linkedln is used for hunting jobs? Maybe I may download an app to understand the recommendation system of Linkedln.
回复删除Yes.You can set up you profile on LinkedIn and get connected with us!Hope we can find suitable jobs soon.
删除Besides showing your portfolio, the endorsement's feature in LinkedIn fortify other's confident when reading your portfolio. I think HRs love this very much :D
删除It is a wonderful sharing that introduces the recommender systems of some famous websites. Now I know how recommender systems are used in Amazon, YouTube and LinkedIn and it is a good extension knowledge for me.
回复删除Thank you for your comment.We can share more knowledge to each other.Besides,your blog is also useful to me and I leaned a lot from you.
删除the recommendation system is fantastic and saves our time of searching things we are interested in sometimes, and the principle of that we learnt on class is closely connected to that.
回复删除Thanks for sharing the examples of recommendation systems used in popular website, after read your blog, I understand the lecture deeper now. By the way, I like the clear architecture of your article, I can know exactly what's inside your blog at the first sight.
回复删除Thank you for sharing these meaningful examples to us. It is really helpful to gather the above recommendation systems here. I can compare the purpose of setting up these recommendation systems and see how the developers make decisions in adding or removing feature from the systems in order to achieve its goals. =)
回复删除your sharing of Recommendation Systems are very interesting.And as a significant part of social media, a good Recommendation Systems will help platform win more user. Thus how to improve user experience of Recommendation Systems is very important.
回复删除Hello, thank you for doing an overview of the different recommender system. What would be interesting is to see what kind of algorithm youtube, amazon, linkedin are using. Compare performance, pros and cons ;)
回复删除Thanks for your sharing! I also found Recommendation Systems very useful. When I type in some key words, the system can generate lots of useful related information to me!
回复删除I think you must do much research about the recommending system of several platforms.
回复删除and we can find may coding package of a website recommending system, which is the reason why so many websites have the almost same recommending mechanism.
Thanks for your sharing! Personalized recommendation is based on the characteristics of the user's interests and purchase behavior to recommended information and commodities to the user that they are interesting.
回复删除I am so glad to see another post having the similar topic with me. As a user of those websites you mentioned, the background algorithm of their recommending system is not opened to us. In my blog, I take a guess about the main idea when they are developing their recommending system based on what we've learnt from the class.
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