Lei feng's network (search for "Lei feng's network" public attention) by writer Xie Defu (micro signal: beancurd191), more than 9 years Internet experience, 6 years relevant experience in personalization, big data. Enter Huawei worked 6 years after graduation, in 2013 start date, focus on large data technology in film and TV, culture, video, entertainment and other fields of application. Vera Bradley phone case
Qian days rumors to was Tencent acquisition of today headlines many people should not strange, I is today headlines of faithful user, why I think today headlines do have than other news/information client better, is because it show to I of content are is I wants to see of, with I in above of behavior increasingly more, it show to I of content more accurate, today headlines using of most core of technology is personalized recommended technology.
With the rise of mobile Internet, many of the behavior gradually shifted from PC end user mobile. People spend more and more time on the phone. People everywhere use mobile phones, when in your car, go to the bathroom, eating, even when you walk in you are using mobile phones. Relative to the PC side, mobile features a narrow screen, user time fragmented. And as the volume of information is increasing, it is hard to find what they want quickly through large amounts of information. This experience was very bad, if you are a product manager, if you face the same problem, hope it is helpful to talk about your next.
What is the recommended engine?
If you bought a book on Amazon, you may encounter such a situation, when added to the shopping basket when you choose a book, it will automatically recommend other books to you. For example: people who bought this book also bought XXXX, you may also like XXXX, combining recommendation, buy the book there are several other combinations can enjoy a favorable combination of the book price. These uses are recommended system, it simply recommended system is the study of all acts of the user on the platform, portraits of users, as well as research platform/product. While matching user and product processes.
Recommended application range of engines?
Recommender systems in a wide range of applications in various fields, such as e-commerce sites, video sites, video-streaming platform, news client, literary sites, music sites, and so on. Below is the recommended system at the famous e-commerce Web sites, video sites, some cases of application and effect.
Why recommendation systems are widely used in various fields?
1, directory or search for ways to find desired content, may have to turn on the small screen of the mobile terminal screen, high cost of finding interesting content, poor user experience.
2, approved the substance of recommendations presented to users, all users are interested in, and each user sees is not the same as Amazon CEO Bezos said, to 1000 users accessing the Amazon to see 1000 different Amazon.
3, users now have very many choices, you can choose the diversity and fragmentation of time users open the phone, such as failing to quickly find content of interest, will be leaving soon.
4, personalized recommendation algorithm for user interests recommend precision, helps users quickly find content of interest, and when you're done with a content, immediately recommend things to you, you can increase user stickiness.
5, to help users find more quality long-tail content, general user access only at the top of the 10% about the contents, many will never sink in not found in the database. Vera Bradley phone cases
6, to help balance the ecological platform, avoiding the Matthew effect, featured content is always to get more exposure, popular content never an opportunity is concerned, make content production ecological polarization.
Architecture and core algorithm for recommender systems
Here I did a product as an example to explain, in architecture, could each do is somewhat different, but used some of core algorithms, you should be the same. How to achieve, product managers do not need to focus so fine, just an idea of the principles can be.
Common algorithms including user preferences in Recommender System algorithms, collaborative filtering algorithm (item_base,user_base), Association Rules algorithms, clustering, similarity algorithm (content_base) and a number of other complementary algorithms. Final analysis of results is as follows:
1, according to users ' preference algorithms figure out users ' interests/product, and then recommend to the user.
2, according to Association Rules algorithms, worked out between support and confidence. The most common application is to mix and match, beer and diapers are classic examples.
3, item_base is in accordance with the collective user behavior to calculate the similarity between the goods and users have read and recommend or buy most similar articles to the user.
4, clustering algorithm based on user clustering or clustering of products. Clustering can be recommended for the category, or continues to calculate the relationship between user and product classes.
5, content_base for relevance calculation is based on the properties of the product itself, calculate the similarity between the goods, the most common kind of recommended applications.
6, user_base calculates the similarity between users is based on the collective behavior, such as calculate a and b are very similar, you can like the contents of b, but not seen, recommended to a.
Common scenarios Home guess you like recommending
Due to the smaller mobile screen, one screen to show less content, and users to find the content they are interested in a screen turned down one screen, show personalized recommendation of content in this place, you can quickly catch the user's eye.
Found in sections recommend content of interest to users, you can give users a pleasant surprise.
Association recommended/featured content page for details: details of the contents page to users recommending similar content with the current content.
Read end/video/live broadcast ends recommend: featured content similar to the current.
Recommended search page when search results, you can recommend its content of interest to users.
Personalized recommendation system in application of several key problems
Personalized recommendation system is a very complex system, which not only issues relating to data processing algorithms and system flexibility, but also robust, data sparsity problem, cold start problems, system accuracy and diversity issues.
1, trash data processing: the exception for system data, junk data services developed a set of rules.
2, cold start issue: due to the precipitation of new users access without data, making it difficult to recommend according to user behavior, currently prevalent method is provides interested labels to guide page when a new user first logs on, guiding the user to set, combined with the other algorithms. A better approach is to use social data for users on other platforms.
3, data sparsity problem: can l dimension calculation using a clustering algorithm, and combining it with other algorithms for recommendation.
4, recommended for accuracy and diversity: through the combination of multiple algorithms recommended. Ensure that the recommended result set accuracy and diversity.
Above is a basic knowledge about the personalized recommender system, and want to help.
This article published by everyone is a network product manager Lei feng. Unauthorized, prohibited reprint!
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