Website Recommendation engine Application understand user needs and habits

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The following is a brief analysis of the application of several representative recommended engines, where two areas are selected: Amazon as the representative of E-commerce, watercress as a social network.

Application of recommendation in e-commerce –amazon

Amazon, the originator of the recommendation engine, has infiltrated the recommended ideas in every corner of the application. The core of Amazon's recommendation is to compare the data mining algorithms with the consumer preferences of other users to predict the products that users may be interested in. In response to the various recommended mechanisms described above, Amazon uses a hybrid partitioning mechanism and displays different recommendations to the user, and the following two graphs show the user's recommendations on Amazon.

Amazon's recommended mechanism-home

Amazon's recommended mechanism-browsing items

Amazon exploits the behavior of all users that can be logged on the site, processes them according to the characteristics of different data, and divides them into different areas to push referrals for users:

Today's recommendation for you: usually buy or view records based on the recent history of the user, and give a compromise recommendation in combination with the popular items.

Recommendations for new products (new for you): using the content-based recommendation mechanism (content-based recommendation) to recommend some new items to the user. In the choice of methods because the new items do not have a lot of user preferences information, so based on the content of the recommendation can be a good solution to the "cold start" problem.

Bundled sales (frequently bought up): The use of data mining technology to analyze the user's purchase behavior, to find often together or the same person to buy the collection of goods, for bundling, this is a typical project based collaborative filtering recommendation mechanism.

Goods purchased/browsed by others (Customers who bought/see the This item Also bought/see): It is also a typical application of collaborative filtering based on projects, which enables users to find items that are of interest to them faster and more conveniently through social mechanisms.

It is worth mentioning that Amazon is making recommendations, the design and user experience is also unique to do:

Amazon uses the advantage of its large historical data to quantify the reasons for the recommendation.

Based on a social recommendation, Amazon gives you factual data to convince the user, for example, how much of the user buys the item;

Based on the recommendations of the article itself, Amazon will also list the reasons for the recommendation, such as: Because your shopping box has a * * *, or because you bought * * *, so you recommend a similar * * *.

In addition, Amazon's recommendations are based on user profile, and user profiles record the user's behavior on Amazon, including looking at those items, buying items, Favorites and Cytopathic list items, and so on, of course, Amazon There is also a way to integrate ratings and other user feedback, which are part of profile, and Amazon provides the ability to allow users to manage their profile in such a way that users can more specifically tell the recommendation engine what his tastes and intentions are. #p # subtitle #e#

Recommended applications in social networking sites – watercress

Watercress is a relatively successful domestic social networking site, it is the book, film, music and city activities as the center, the formation of a pluralistic social networking platform, the nature of the recommended function is essential, below we see how the watercress recommended.

The recommended mechanism of watercress-watercress film

When you're in a watercress movie, add some movies that you've seen or are interested in to the list that you've seen and want to see, and give them a corresponding rating, when the Watercress recommendation engine has got some of your preference information, then it will show you as the film recommended above.

Recommended mechanism for watercress-based on user taste recommendations

Watercress recommended through the "watercress guess", in order to let users know how these recommendations are coming, watercress also gave a "watercress guess" a brief introduction.

"Your personal recommendation is automatically based on your collection and evaluation, and everyone's list of recommendations is different," he said. The more you collect and evaluate, the more accurate and rich your recommendation will be.

The recommended daily content may vary. With the growth of watercress, the content recommended to you will be more and more accurate. ”

This allows us to know clearly, watercress is necessarily based on the social collaborative filtering recommendations, so the more users, users feedback more, then the recommended effect will be more and more accurate.

Compared to Amazon's user behavior model, the Watercress film model is simpler, that is, "see" and "want to see", which also makes their recommendations more focused on the user's taste, after all, the motives of shopping and watching movies are still very different.

In addition, Watercress also has based on the recommendation of the article itself, when you check the details of some movies, he will recommend to you "like the movie people like the movie", as shown below, this is a collaborative filter based application.

The recommended mechanism of watercress-based on the recommendation of the film itself

Summary

In the era of network data explosion, how to let users find the desired data faster, how to let users find their potential interests and needs, whether for e-commerce or social network applications are critical. The emergence of the recommendation engine makes the issue more and more interesting. But for most people, you may wonder why it always guesses what you want. The magic of the recommendation engine is that you don't know what the engine is recording and reasoning behind this recommendation.

Through this review article, you can understand that in fact, the recommendation engine is just silently record and observe your every move, and then by all the users generated by the mass of data analysis and discovery of the rules, and then slowly understand you, your needs, your habits, and silently silent to help you quickly solve your problems, Find what you want.

In fact, think back, a lot of times, the recommendation engine knows you better than you.

Through the first article, I believe you have a clear first impression of the recommendation engine, the next article in this series will delve into the recommendation strategy based on collaborative filtering. In today's recommended technology and algorithms, the most widely accepted and adopted is based on collaborative filtering recommendations. It is simple in its method model, data dependence is low, data is convenient to collect, the recommendation effect is superior to many advantages to become the recommended algorithm "No.1" in the public eye. This paper will take you deep into the secret of collaborative filtering and give an efficient implementation of the cooperative filtering algorithm based on Apache Mahout. The Apache Mahout is a more recent open source project for ASF, which originated in Lucene, built on Hadoop, and focused on the efficient implementation of machine learning classical algorithms on massive amounts of data.

Thank you for your attention and support for this series.