Journal of Geo-information Science >
Spatial Cascaded Model for Personalized Recommender System
Received date: 2015-04-10
Request revised date: 2015-07-20
Online published: 2016-02-04
Copyright
Recommendation system has become mature and been successfully applied in many fields since its emergence. Due to the popularization of different types of mobile terminals, spatial information is brought into the recommendation systems. However, the existing researches mainly focus on spatial locations and rarely consider spatial relations. Meanwhile, the existing recommendation algorithms usually consider only the user’s history behaviors but not the influence of future behaviors on current recommendations. According to the activity chain theory, future activities have an impact on the current behavior as well as the past activities did. If a user has two steps of information retrieval, and the second step is based on the result of the first step, he would choose an item from the result which is convenient for him to make the second choice, and thus he can get the best choices at both steps of retrieval. That is to say, the current selection would be affected by the next retrieval of information. In this paper, we model the spatial distribution of the travel targets by considering one’s future intentions as well as the past data, and propose a spatial cascaded model for personalized recommender system. The model is built for situations with a series of continuous choices in the spatial space based on the traditional recommendation algorithm and the influence of future activities. The influence of spatial relation is introduced into the traditional recommendation algorithm as a distance decay function. In order to prove the feasibility of spatial cascaded recommender system, a restaurant recommender system is developed based on the proposed model. Taking into account of user’s preference and distance, a cost-benefit index was proposed to evaluate the result. The result shows that when considering further activities and spatial relations in recommendation, the system can produce a more reasonable result.
LI Aoyong , XU Jun . Spatial Cascaded Model for Personalized Recommender System[J]. Journal of Geo-information Science, 2016 , 18(2) : 160 -166 . DOI: 10.3724/SP.J.1047. 2016.00160
Fig. 1 The framework of spatial cascaded recommender system图1 空间级联模式下的个性推荐模型框架 |
Fig. 2 The schematic of spatial cascade图2 空间级联示意图 |
Fig. 3 The schematic of spatial cascade model图3 空间级联模型示意图 |
Fig. 4 Spatial cascaded recommender system图4 空间级联模式下的个性推荐模型 |
Fig. 5 The spatial distribution of restaurants and KTVs图5 餐厅、KTV空间分布图 |
Fig. 6 Pseudo code of spatial recommender algorithm图6 空间级联模式下的推荐模型代码 |
Fig. 7 Pseudo code of spatial cascade function图7 空间级联函数代码 |
Tab. 1 The evaluation of spatial cascaded recommender algorithm表1 空间级联推荐评价表 |
编号 | X坐标 | Y坐标 | p |
---|---|---|---|
1 | 637 870.9959 | 3 452 923.334 | 0.215 |
2 | 641 123.6277 | 3 457 946.251 | 0.649 |
3 | 640 512.8528 | 3 455 670.962 | 0.361 |
4 | 644 801.1710 | 3 465 454.573 | 0.085 |
5 | 641 963.7390 | 3 459 558.950 | 0.079 |
Fig. 8 The comparison of spatial cascaded recommender system and traditional recommender system图8 空间级联推荐模型与传统推荐方法结果的比较 |
The authors have declared that no competing interests exist.
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