Session recommendation method considering repeated purchase behavior
A recommendation method and behavior technology, applied in the field of Internet services, can solve problems such as not considering the long-term interests of users
Active Publication Date: 2021-05-18
浙江知多多网络科技有限公司
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Problems solved by technology
But in the method (method), the recommended repeated items are recommended in the item sequence in the current session, and the possibility of the user repeatedly buying the same item within a short period of time, that is, within a session is very low
And this method does not take into account the user's long-term interest, which tends to be static
If the short-term interest of the user in the current session and the long-term interest of the user are highly repetitive, the probability of repeated purchase behavior is high
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[0042] In order to further understand the present invention, a conversation recommendation method that considers repeated purchase behavior provided by the present invention will be specifically described below in conjunction with specific embodiments, but the present invention is not limited thereto. The non-essential improvements and adjustments still belong to the protection scope of the present invention.
[0043] A session refers to a sequence of items that a user interacts with within a period of time, and a session recommendation task refers to predicting the next item that the user is interested in in the current session based on the item sequence in the current session. A user's current session can be denoted as S T ={x 1 , x 2 ,...,x τ ,...,x t}, where x τ represents the τth item. Predicting the next item of interest for the user in the current session can be expressed in mathematical language as P(x t+1 |S T ), where P(x t+1 |S T ) represents the current s...
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The invention discloses a session recommendation method considering repeated purchase behaviors. According to the method, a model is established for modeling a current session and long-term interests of a user, and the next clicked article of the user is predicted. A user often has a fixed behavior habit on an online platform, so that the recommendation method considering the repeated purchase behavior mainly comprises four parts: firstly, acquiring short-term and long-term interest characterization and auxiliary behavior (purchase) characterization of the user according to a current interaction session sequence of the user; secondly, selecting a repetition mode or an exploration mode according to short-term and long-term interest characterization of the user; thirdly, performing recommendation in a sequence of articles historically purchased by the user in a repetition mode, according to short-term and long-term interest characterization and auxiliary behavior (purchase) characterization of the user; and fourthly, recommending articles not in the sequence of articles historically purchased by the user according to the short-term and long-term interest characterization of the user in the exploration mode.
Description
technical field [0001] The invention belongs to the technical field of Internet services, and in particular relates to a session recommendation method considering repeated purchase behaviors. Background technique [0002] With the development and popularity of online services, online platforms record a large amount of user behavior data. Find out the items that users are most interested in from massive data and recommend them to users, which can greatly improve user satisfaction and company revenue. At this time, the recommendation system is very important. The recommendation system can dig out the user's favorite items from a large number of items. [0003] Traditional recommendation methods mainly include three types. One is a general recommendation system, which mines the static association between users and items from the historical interaction data between users and items, including methods such as collaborative filtering and matrix decomposition. Such methods input...
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IPC IPC(8): G06Q30/06G06F16/9536G06F16/2458G06F16/332G06K9/62G06N3/04G06N3/08
CPCG06Q30/0631G06F16/9536G06F16/2465G06F16/3329G06N3/08G06N3/048G06N3/045G06F18/2415
Inventor 顾盼
Owner 浙江知多多网络科技有限公司