For mobile Internet applications, in addition to the vast amount of information accumulated by the traditional Internet, a variety of rich applications available, the mobile Internet itself has produced a huge amount of content and applications, how to accurately identify the user's preferences and on this basis to recommend the most relevant products, services, information is to solve the so-called "information overload" One of the effective means of the problem.
Whether based on content filtering (content-based filtering), collaborative filtering (collaborative filtering) based personalized recommendation technology, personalized recommendations based on the Internet are basically based on user behavior history (purchase behavior, click Behavior, Collection behavior, comment behavior, and so on) + current content scenarios (such as books being browsed, are communicating SNS friends, etc. to recommend the context, you can say that the Internet personalized recommendation is mainly focused on the online service itself, for the user's position factors and related factors are not too much consideration.
Personalized recommendations based on location-based services seem more interesting than personalized recommendations based on the Internet. Personalized recommendation based on location-based service differs from the personalized recommendation based on Internet, in addition to the position factor, but also includes the time factor, the human factor, according to the 1h5w frame to comb the personalized recommendation based on position service, after introducing the position factor, the human factor, the time factor, Personalized recommendations can be a lot of interesting application scenarios, most importantly, online services and offline traditional services to the integration and interaction.
The personalized recommendation of the traditional Internet is mainly limited to the relative ratio of the content of the websites, and the other important factor of the mobile Internet personalization is the open platform. For the mobile Internet itself because of the accumulation of traditional Internet rich applications available, in the face of the inherent advantages of traditional internet giants, mobile internet era of personalized recommendations should not only be complacent, should adopt a more open mind, do not expect in the mobile internet era to rebuild a new platform, For users to be fully glued to the above, users should be integrated on the Internet using a variety of existing applications and user data as a basis for the recommendation. Of course, a large part depends on the openness of the traditional internet giants, but openness is the spirit and inevitable trend of internet and mobile Internet.
It can be said that personalized recommendations based on location-based services are more technical requirements than the personalized recommendation of the traditional Internet for recommendation engines. If location service is the killer application of mobile Internet, personalized recommendation based on location-based service is one of the most core technologies.
1, personalized recommended 1h5w1.1, when time
O past a while
o Present
O will
1.2. where
o at what location
O Location around
1.3. Who characters
o I
O my friend
O and my strangers, but like-minded.
1.4, what what
o what I have done/what I am doing/what I want to do
o What my friends have done/what they are doing/what they want to do
O and what I have not met like-minded have done/are doing/want to do
o What has happened/what is happening/what is going to happen.
o What Hot
1.5, why why
o Get information/disseminate information
o Make Friends
O for fun
O Make a deal
o Bored Wandering
O, and so on
1.6. How To
o How to get the information I need quickly and easily
o How to quickly and easily complete information sharing
o How to make it easy for me to find friends of interest
o How to make it easy for me to find interesting things I like
o How to make my transaction easy and quick
o How to surprise me in my wandering
O, and so on
2, personalized recommended information source2.1. Current Location information
2.2, the surrounding business information
2.3, the surrounding marketing information
O Promotions
o Second Kill
O Advertising
o Lottery
o Interactive Games
O, and so on
2.4. Activities around
O Party
o Meeting
o Concert
o the Viewer
O Collection
Group O
O, and so on
2.5, surrounding the unexpected events
2.6, SNS information
o Friends of Accessories
o interests of like-minded people nearby
O Circle Around
2.7. Social media information
O Weibo
o Square
o Note
2.8. Transaction information
Group O
O Collection
o Purchase
O Exchange
O, and so on
2.9. Personal information
o Current Position
O Activity Trajectory
O Status
O Weibo
O Collection
o Friend
O Concerned person/merchant/Event/Events
O Participating Activities
O Participating Transactions
O Participating Circles
O, and so on
2.10, the list
O Hot Events List
o List of business activities
O Weibo rankings
O Tide People List
O Community Rankings
O Funny List
O, and so on
Source: http://www.yeeach.com
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