Time-aware adaptive interest point recommendation method based on K-means clustering

Abstract

The invention discloses a time-aware adaptive interest point recommendation method based on K-means clustering. The method comprises the following steps: 1, converting a sign-in data set into a three-dimensional scoring matrix; 2, counting the number of sign-in users, the number of accessed interest points and sign-in times in each time slot, and constructing a three-dimensional sign-in feature vector of each time slot; 3, performing K-means clustering on the time slots, and calculating the time similarity between the time slots in the same cluster; 4, calculating the user similarity at the current time by using the score information in other time slots in the same time cluster; step 5, improving a traditional user-based collaborative filtering method by using a time clustering result and time similarity in a cluster, so that the traditional user-based collaborative filtering method can adaptively generate interest point prediction scores according to current recommendation time; and 6, comparing the recommendation precision of the recommendation system provided by the invention with the recommendation precision of other classic recommendation systems, and evaluating the accuracy and effectiveness of the provided technology.

Description

technical field [0001] The invention relates to a time-aware adaptive interest point recommendation method based on K-means clustering in a location social network, and belongs to the technical field of artificial intelligence and machine learning. Background technique [0002] In recent years, with the rapid development of communication technology, positioning technology and mobile Internet technology, location-based social networks (Location-based Social Networks, LBSNs) have become a new form of media for people to share and transmit information. The real world below provides a platform for close contact. At present, there are a large number of mature location-based social network platforms at home and abroad, such as Facebook, YouTube, Twitter, Weibo, Douban, Dianping, Meituan and WeChat Moments. In location-based social networks, users can establish complex social relationships, such as friend relationships, colleague relationships, relative relationships, etc.; use th...

AI Technical Summary

Problems solved by technology

Most point-of-interest recommendation technologies are not good at dealing with dynamically changing user needs, it is difficult to support the correction and adjustment of user preferences over time, and it is impossible to provide real-time point-of-interest recommendation results that best meet the current time situation

[0008] (2) Ignoring the time-dimensional dynamic characteristics of user similarity

Therefore, using the global user similarity at different times is not in line with the factual law

[0009] (3) The problem of data sparsity in the three-dimensional matrix of user-time-point of interest

[0010] The above is the deficiency of the existing time-aware point of interest recommendation technology, which brings great disadvantages in the design, development, deployment and operation of different social network platforms, especially in the network platform of massive project information. The decline of system service quality will affect the sales performance of e-commerce system

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