Company dynamics

AI stumbling on content distribution



These breeders are trained professionals, jargon called --- by the site editor for the user to set the agenda, according to the taste of most users pick content.


Later, the editor is too busy to use the machine to help --- the most simple way is the machine is "popular recommendation", such as according to the amount of traffic ｏｒ other data to sort.






To be precise, this is the content of the distribution of C2M, it is a single user for the purpose of communication, out of the mass communication / Focus spread pattern, is not enough to leather all the search engines and portal life?


This kind of intelligent content C2M has a profound background of the times. Today, you have stood on the edge of the times, watched AI technology lit the IOT lead, then you will find yourself unquestionable access to the next information nuclear explosion era: information terminal explosion, information scale explosion, information platform explosion……










Content industry is facing C2M revolution, okay?






The real situation, you may be unexpected.




As an independent research direction, the ｓｏｕｒｃｅ of the recommended system can be traced back to the early 90s collaborative filtering algorithm, the middle of the representative of the traditional machine learning algorithm, such as Netflix contest promoted by the semantic model, now is more complex depth Learning model.




(A) collaborative filtering faltering start


In accordance with the terms of the encyclopedia, collaborative filtering is the use of user groups preferences to recommend information for you interested in these users either interested in co-operation, ｏｒ have a common experience, and then the site with your feedback (such as score), filter analysis, and then Help others filter information.




Initially, collaborative filtering applies to mail filtering.










In 1998, Amazon's Linden and his colleagues applied the article-to-item patent for article-based technology, which was the classic algorithm used early in the Amazon, once detonated.






(B) the traditional machine learning began to accelerate








Our users add millions of videos to their playlists every day.




A year after the start of the match, Korbell's team won the first stage with 8.43% improvement. They have paid more than 2,000 hours of effort, the integration of 107 algorithms. Two of the most effective algorithms are matrix decomposition (usually called SVD, singular value decomposition) and localized Boltzmann machine (RBM).








The implicit semantic model utilizes the clustering analysis method, and one of the big advantages is that it can do both the prediction and the text content at the same time, so that the recommended effect can be greatly improved through the content.


The traditional method of analysis is not high in the user's label, and according to the label mapping to the results of the two steps. Such as the user to fill the age is not necessarily true, ｏｒ not all young people like comics. The core of the implicit semantic model is to transcend the dimension of these superficial semantic labels, and to explore the deeper association of user behavior through machine learning technology, which makes the recommendation more accurate.








(C) the depth of learning to bring "unmanned"




In the face of this problem, YouTube and Facebook put forward a new solution: the use of in-depth learning, manufacturing smart machines. Over the past decade, the depth of learning has made a huge leap, for the settlement of large amounts of data more advantages.




In YouTube, for example, the latest published recommendation system algorithm consists of two neural networks, one for candidate generation and one for sorting. First, the user's browsing history as input, candidate generation network can significantly reduce the number of recommended video, from a large library to ｓｅｌｅｃｔ a group of the most relevant video.






Second, the content industry is about to be C2M subversion


(ATM) of a bank in the city of Corpus Christi, Texas, USA, spit out the words "save me" on 11th, and the news quickly spreads over the Chinese network , Become the headlines of many sites.


Do you need to see exactly the same article from N sites?


These redundant information consumes your energy and traffic, just as you open any TV channel, can see a lot of instant noodles advertising, it is difficult from a lot of information quickly find what they want.


How to solve the embarrassment of user information redundancy?


In the past there have been many unsuccessful technical programs, personal portal short-lived, RSS subscription is not a climate, cross-site tracking can not stand. Can lead the future, only C2M.


C2M mode can be applied to the whole network as a headline today, ｏｒ a platform based on Facebook. Its core is based on user behavior habits, characteristics and demands, the massive information to extract, sort and then passed to the user, which is to overcome the pain point of the secret.


But the voice of questioning a lot. Such as the view that synergistic filtering such a recommendation is easy for users to form information cocoon room, can not identify reading scenes, real-time poor, time-consuming and other shortcomings, and today's headlines such patterns are often criticized, but also difficult to capture the user interest , User data privacy and management and many other challenges.




1. The reasons for support are as follows:


① thousands of thousands of people, all adjustable.


The personalized content recommendation mechanism can recommend information based on the user's preferences. Through a variety of algorithms, by analyzing the user's historical behavior, compare the relevant users and related items guess the user may like the content, list the candidate set and verify, the user can get more accurate content, so that the distribution of information to do thousands of people , To achieve the content and the user's precise connection, rather than the traditional sense of thousands of people like the delivery.


② sea fishing needle, improve efficiency




③ cast its good, enhance the sticky




④ digging long tail, breaking the poles




Contact: 南平市香水有限公司

Phone: 13800000000

Tel: 400-123-4567


Add: Here is your company address