Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++

Abstract

Recommendation system for tourist spots has very high potential value including social and economic benefits. The traditional clustering algorithms were usually used to build a recommendation system. However, clustering algorithms have the risk on falling into local minimums, which may decrease the final recommendation performance heavily. Few works focused their research on tourist spots recommendation and few recommendation systems consider the population attributes information for fitting the user implicit preference. To address the problem, we focused our research work on designing a novel recommendation system for tourist spots. First a new dataset named “Smart Travel” is created for the following experiments. Then hierarchical sampling statistics (HSS) model is used to acquire the user preference for different population attributes. A new recommendation list named LA is generated in turn by fitting the excavated the user preference. More importantly, SVD++ algorithm rather than those traditional clustering algorithms is used to predict the user ratings. And a new recommendation list named LB is generated in turn on the basis of rating predictions. Finally, the two lists LA and LB are fused together to boost the final recommendation performance. Experimental results demonstrate that the mean precision, mean recall, and mean F1 values of the proposed recommendation system improve about 7.5%, 6.2%, and 6.5%, respectively, compared to the best competitor. The novel recommendation system is especially better at recommending a group of tourist spots, which means it has higher practical value.

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

“…Most of the studies relevant to tourist spot recommendations focus on few parameters. In the works of [87,88], only user preferences are considered. In the work of [89], two parameters such as user preferences and sentiments are considered.…”

Tour Route Recommendation Model by the Improved Symmetry-Based Naive Bayes Mining and Spatial Decision Forest Search

“…It recommends personalized attractions by calculating the similarity between tourists and candidate attractions in the potential tourism spaces. Li et al [7] established a POI recommendation method based on stratified sampling statistics and singular value decomposition. The stratified sampling statistics are used to obtain the user preferences for different group attributes, and the singular value decomposition method is used to predict the user scores.…”

“…The once-visited POIs and tourist interest weights collected in the experiment are: P b(1) West Lake in Hangzhou (0.5); P b(2) Henan Museum (0.8); P b(3) Suzhou Gardens (0.7); P b(4) Zhengzhou Zhongyuan Wanda Plaza (0.1); P b (5) Xi'an Yanta Square (0.7); P b (6) Pingyao Ancient City (0.6); P b (7) Qinghai Lake (0.4); P b (8) Wangfujing, Beijing (0.2); P b (9) Yu Garden in Shanghai (0.5); P b (10) The Taihu Lake (0.6); P b (11) Xi'an Vientiane City (0.2); P b (12) Huangguoshu Waterfall (0. (3) Use the geospatial data of Chengdu as the constraint.…”

Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

“…Li G. et al [26] proposed a combination of the Singular Decomposition Method and statistical methods for the operation of the travel guide system, but theydidnot present an adaptation of the SVD method for use in distributed systems.Guo X. et al [27] proposed a method of combining element attribute information with a historical rating matrix to predict potential user preferences. This method combines attribute and temporal information in a decomposition matrix model.…”