Asking Images: Hybrid Recommendation System for Tourist Spots by Hierarchical Sampling Statistics and Multimodal Visual Bayesian Personalized Ranking

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.

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Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation

Intelligent Data Analysis ◽

10.3233/ida-205388 ◽

2021 ◽

Vol 25 (4) ◽

pp. 1013-1029

Author(s):

Zeeshan Zeeshan ◽

Qurat ul Ain ◽

Uzair Aslam Bhatti ◽

Waqar Hussain Memon ◽

Sajid Ali ◽

...

Keyword(s):

Recommendation System ◽

Sources Of Information ◽

Multiple Features ◽

Recommendation Algorithms ◽

Label Correlations ◽

Feature Based ◽

Multicriteria Model ◽

Implicit And Explicit ◽

Hybrid Recommendation ◽

Different Sources

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.

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