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A method and device for item recommendation
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A recommended method and item technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as complex calculations, inability to meet the needs of processing big data, and difficult implementation
Active Publication Date: 2018-06-22
TCL CORPORATION
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[0007] The embodiment of the present invention provides an item recommendation method and device, aiming to solve the item recommendation method provided by the prior art, the calculation is complex, the efficiency is low, and it is difficult to implement in a distributed environment, so it cannot meet the actual processing of large data question of needs
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Embodiment 1
[0077] figure 1 The implementation process of the item recommendation method provided by Embodiment 1 of the present invention is shown, and the details are as follows:
[0078] In step S101, item attribute data and user log data are collected, and the user log data includes user historical behavior data.
[0079] In the embodiment of the present invention, item attribute data and user log data are collected. Wherein, the items may refer to movies or other commodities, for example. For the convenience of description, in this embodiment, a movie is used as an example to illustrate the referred item.
[0080] The collected item attribute data includes the basic attributes of the movie, such as type, director, actor, country of production, language and other attribute data.
[0081] The collected user log data includes the user's historical behavior data of watching movies, such as movie title, movie full time, watching time, watching time point, etc.
[0082] When new movies...
Embodiment 2
[0162] figure 2 A specific structural block diagram of the item recommendation device provided by Embodiment 2 of the present invention is shown, and for convenience of description, only parts related to the embodiment of the present invention are shown. The item recommendation device 2 includes: a data collection unit 21 , a two-dimensional similarity calculation unit 22 , a preference calculation unit 23 and an item recommendation unit 24 .
[0163] Wherein, the data collection unit 21 is used to collect item attribute data and user log data, and the user log data includes user historical behavior data;
[0164] A two-dimensional similarity calculation unit 22, configured to calculate the two-dimensional similarity between items according to the item attribute data and the user historical behavior data;
[0165] A degree of preference calculation unit 23, configured to calculate the user's degree of preference for each item according to the two-dimensional similarity;
[...
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Abstract
The invention is applicable to the technical field of networked information retrieval and provides an item recommendation method and device. The method includes that item attribute data and user journal data are collected, and the user journal data include user history behavior data; two-dimension similarity among items is calculated according to the item attribute data and the user history behavior data; degree of preference of a user on each item is calculated according to the two-dimension similarity; corresponding items are recommended to the user according to the degree of preference. Compared with item recommendation methods and devices in prior art which merely considers the item attribute data or the user history behavior data to obtain attribute similarity or behavior similarity, the item recommendation method and device has the advantages that the item attribute data and the user history behavior data are comprehensively considered to obtain two-dimension similarity data when the similarity among items is calculated, the accuracy is higher, and the extremely preferred items can be recommended to the user.
Description
technical field [0001] The invention belongs to the technical field of network information retrieval, and in particular relates to an item recommendation method and device. Background technique [0002] With the development of the Internet and the explosive growth of network resources, the cost for Internet users to obtain information is getting higher and higher. Although users can use search engines to obtain information, there are still deficiencies. [0003] On the one hand, it is difficult for users to find suitable keywords to describe the search content; on the other hand, it is difficult for the results returned by search engines to meet the personalized needs of users. [0004] Therefore, a personalized recommendation system emerges as the times require, which analyzes user preferences based on user behavior or item attributes, thereby providing users with personalized recommendation results. When a personalized recommendation system makes recommendations to users...
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