Set sorting music recommendation method for implicit feedback data of user

A technology of implicit feedback and recommendation method, applied in electronic digital data processing, special data processing applications, digital data information retrieval, etc., can solve the problems of failing to discover the sequence relationship of song collections, ignoring user contact, etc., and achieve good music. The effect of the recommended effect

Pending Publication Date: 2021-07-09
XI AN JIAOTONG UNIV
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Problems solved by technology

However, this method of rank ordering assumes that each user is independent, ignoring the connection between users
In addition, s

Method used

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  • Set sorting music recommendation method for implicit feedback data of user
  • Set sorting music recommendation method for implicit feedback data of user
  • Set sorting music recommendation method for implicit feedback data of user

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Experimental program
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Embodiment 1

[0065] see figure 1 , according to the user-song interaction data, that is, the implicit feedback data can be constituted as figure 1 The user-song interaction matrix in (a), where the check mark represents that the user has listened to the song. Users with similar behaviors may also have similar preferences. According to the user-song interaction data, find out the user's nearest neighbor users, see figure 1 In (b), the songs that the user to be recommended has not heard but his similar users have listened to are regarded as a collection of songs that the user may like. At this time, for the user to be recommended, all songs can be divided into three types of song collections.

[0066] Table 3 is the data set used in the experiment of this method, which contains data of different scenarios, different scales and statistical characteristics.

[0067] Table 3 Dataset

[0068]

[0069] For each data set in table 3, all adopt the method in the present invention, image 3 It...

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Abstract

A set sorting music recommendation method for user implicit feedback data comprises the steps of obtaining user-song interaction data, then finding out a nearest neighbor of a user waiting for recommendation songs from all users of music software by adopting a MinHash-based locality sensitive hashing algorithm, then mining potential preference songs of the user waiting for recommendation songs according to a collaborative filtering algorithm, dividing all songs into three types: heard songs, potential preference songs and remaining songs; and sequencing the listened songs, the potential preference songs and the remaining songs, generating a personalized music recommendation list, and completing music recommendation. According to the method, the cooperative relation between the users can be utilized, the sequence relation between the song sets is considered, and compared with a traditional method, a better music recommendation effect is achieved.

Description

technical field [0001] The invention relates to the field of music recommendation systems, in particular to a music recommendation method for set sorting of user implicit feedback data. Background technique [0002] The music recommendation system recommends songs that the user may like based on the user's feedback on the song. The user's feedback is divided into two forms: explicit feedback and implicit feedback. Explicit feedback is the user's rating of the song, usually numerical (from 0-10 or 0-5). Implicit feedback represents a kind of behavior data of the user, which is usually binary (0 or 1), that is, whether the user has had this behavior, that is, whether the user has listened to the song. Since implicit feedback is more common and easier to collect than explicit feedback, how to deal with implicit feedback has attracted a lot of attention in recent years. At present, most of the models dealing with implicit feedback are pairwise sorting models, and this type of ...

Claims

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Application Information

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IPC IPC(8): G06F16/635
CPCG06F16/635Y02D10/00
Inventor 王晨旭杨煜郭晨野索凯强管晓宏
Owner XI AN JIAOTONG UNIV
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