Research On Point-of-interest Recommendation Algorithms Based On GRU Model

With the rapid development of the mobile Internet,some Location-based Social Network(LBSN)platforms are becoming more and more popular.Users of these LBSN platforms will check in at geographic locations,share location,and activity information,which will generate a large amount of check-in data.These data can give birth to a new recommendation servicelocation recommendation.In a geographic information system,locations that have unique identifications such as restaurants,theaters,and stadiums that reflect user preferences are called points-of-interest(POI).Therefore,location recommendation is also called POI recommendation.Users can easily share location-related content at a certain point of interest,and many of these content can be used in point-of-interest recommendations to improve the user experience of the LBSN platform.Among the existing points of interest recommendation methods,there is a lack of effective methods for constructing high-quality negative samples of user check-in records.When discussing the impact of time factors on user points of interest recommendation,either the granularity of capturing the time feature is relatively rough,the method used is relatively traditional,and high-dimensional features cannot be captured;or it is only based on the recent check-in records of the check-in sequence,without considerating the time cyclical characteristics formed by the long-term check-in sequence of the user having an impact on their preference in terms of the difference in time.In response to the above problems,this paper designs a point-of-interest recommendation model based on the GRU model—Time4Pre.The model first encodes the user check-in time to generate a time stamp in the data preprocessing stage,and then generates a characteristic time stamp for the point of interest,and generates a high-quality negative sample sequence using the characteristic time stamp of the point of interest.Then it captures the user's sequence preference and time preference based on the GRU model.The capture of sequence preference uses GRU as the basic model,and the capture of time preference is based on the time stamp interval of two check-in records,fully taking into account the time periodic characteristics formed by the user's long-term check-in sequence having an impact on their preference in terms of the difference in time.Finally,the sequence preference and time preference are merged from both linear and non-linear perspectives to recommend geographic points of interest for users.The specific contributions of this article are as follows:(1)Two public check-in record data sets are analyzed in detail from the perspectives of time influence and sequence influence,which can draw the following conclutions:In terms of time influence,1)the regularity of the user's check-in records on workdays is stronger than that on weekends;2)the regularity of the time interval between any two checkin records is weaker than that of two consecutive check-in records,whether it is a workday or a weekend.3)users are more inclined to check-in with points of interest that have been checked-in with a shorter time interval,and this time correlation should be closer in continuous check-in records.The above findings indicate that there is an obvious relationship between the user's check-in behavior and the time interval of the check-in record.In terms of sequence influence,the user's continuous check-in behavior shows obvious sequential characteristics.The user's multiple consecutive check-in POI records will have an impact on the user's check-in behavior,and two consecutive check-in POI records have the greatest impact on the user's check-in behavior.(2)A method of generating negative samples based on the characteristic timestamp of interest points—Time character method is proposed.In the data preprocessing stage of the Time4 Pre model,based on the time stamp generated by the user's check-in time,the characteristic time stamp is generated for the points of interest,and then the characteristic timestamp of the point of interest is used to construct a negative sample sequence of the user's check-in record.Experimental results show that this method can not only improve the quality of negative sample sequences,but also ensure the efficiency of negative sample sequences when used for training,which is also helpful to improve the recommendation effect of the model.(3)Capturing the user's time preference and sequence preference and integrate the two.The user's check-in record has obvious time periodic characteristics.Different from the previous fuzzification of periodic characteristics of time,the Time4 Pre model is based on the check-in record timestamp interval reflecting the difference of check-in time,treats weekends and working days differently,and captures the user's sequence preference while capturing the user's time preference,and then use linear and non-linear methods to fuse these two preferences.Linear method's parameters are simpler than nonlinear methods,but non-linear method can better adapt to complex application scenarios.Based on two public check-in data sets,this paper designs a large number of experiments to verify the effectiveness of the proposed model,and explores the influence of different influencing factors on the experimental results.Experiments show that the method of capturing user time preference is very effective in improving the recommendation effect of the model,and the experimental effects of the two fusion methods are better than the best model currently.