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Continuous interest point recommendation method based on check-in time interval mode

A time interval, recommendation method technology, applied in the field of recommendation systems, can solve the problem of not considering the diversity of user behavior patterns

Active Publication Date: 2019-03-19
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

In fact, users of different occupations have different office hours, and their corresponding behavior patterns are also different, but the current research work does not take into account the diversity of user behavior patterns

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  • Continuous interest point recommendation method based on check-in time interval mode
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  • Continuous interest point recommendation method based on check-in time interval mode

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Embodiment Construction

[0062] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0063] image 3 For the check-in data of New York City in the Foursquare dataset, it describes the relationship between the user's preference for points of interest (Probability) and the time interval (Transition Interval (hr.)). in, image 3 (a) shows the probability distribution of a user visiting a restaurant (Food) and a nightclub (Nightlife) as the time interval changes after signing in to the workplace (Work). We found that when the time intervals were 4 hours, 12 hours and 23 hours, the probability of users transferring from the workplace to the restaurant reached a maximum value. This observation illustrates that people usually eat lunch after 4 hours of work, dinner after 12 hours of work, and breakfast 1 hour before work. In addition, the peak of users signing in at nightclubs occurs about 10 hours after work, which indicates that people...

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Abstract

The invention relates to a continuous interest point recommendation method based on check-in time interval mode, belonging to the recommendation system field. According to the check-in data of each user, synthesizing personalized preference, geographic distance preference and check-in time interval preference to form the user's comprehensive preference for the next interest point to be visited, and adopting the third-order tensor model to model the continuous check-in behavior; a probabilistic model is constructed to learn the comprehensive preference degree of the user to the points of interest by taking the check-in interval preference as a potential variable. In the parameter learning phase, the expectation maximization algorithm is designed to optimize the parameters of the probabilitymodel, and finally the task of recommending the next point of interest for users to visit is realized. The tensor / matrix factorization algorithm is used to compensate the missing information in the tensor and matrix. Compared with the prior art, the method of the invention effectively solves the problem that the method of the invention effectively solves the sparsity problem of the user-point ofinterest sign-in matrix, and provides the user with an accurate and efficient continuous interest point recommendation service.

Description

technical field [0001] The invention relates to a method for recommending continuous points of interest, in particular to a method for recommending continuous points of interest based on a check-in time interval mode, and belongs to the field of recommendation systems. Background technique [0002] In recent years, Location-based Social Networks (LBSNs), such as Foursquare, Gowalla, GeoLife, etc., have developed rapidly, enabling users to share their check-in experience online. Point-of-interest recommendation becomes more important and practical, which not only helps users find favorite points of interest, but also helps enterprises obtain more target customers. At present, many research institutions have carried out research on POI recommendation tasks. However, since each user's check-in data is highly sparse, it is challenging to achieve accurate POI recommendation tasks. Current research work considers all check-in data as a whole, and the sequential information of us...

Claims

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Application Information

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IPC IPC(8): G06F16/9537G06F16/9535
Inventor 礼欣江明明石雨
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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