A Novel Approach towards Tourism Recommendation System with Collaborative Filtering and Association Rule Mining

Abstract

In the tourism recommendation system, the number of users and items is very large. But traditional recommendation system uses partial information for identifying similar characteristics of users. Collaborative filtering is the primary approach of any recommendation system. It provides a recommendation which is easy to understand. It is based on similarities of user opinions like rating or likes and dislikes. So the recommendation provided by collaborative cannot be considered as quality recommendation. Recommendation after association rule mining is having high support and confidence level. So that will be considered as strong recommendation. The hybridization of both collaborative filtering and association rule mining can produce strong and quality recommendation even when sufficient data are not available. This paper combines recommendation for tourism application by using a hybridization of traditional collaborative filtering technique and data mining techniques.

A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

“…end if (8) end for (9) if (Rinde x i ≥ λ) then (10) dn i ⟶ send positiveLibrary; (11) end if (12) if (Rinde x i ≤ c) then (13) dn i ⟶ send negativeLibrary; Mobile Information Systems composed of linear cells. We use the method of random gradient descent [31] to update the model parameters, and the generated word embedding is used to measure the semantic similarity of the input keyword.…”

A Methodological Workflow for Deriving the Association of Tourist Destinations Based on Online Travel Reviews: A Case Study of Yunnan Province, China

“…Qi and Wong (2014) adopted Apriori algorithm association rules mining to segment Macau's tourists and to predict tourists' preferences for the different local heritage attractions [47]. In addition, the association rule technique could also be utilized to develop a tourism recommendation system [48]. Therefore, it is feasible to transfer the association rule data mining technique to uncover the frequent tourist patterns.…”

A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

“…Researchers consider introducing various auxiliary information into the recommendation model to build better hybrid models. For instance, the studies in [11][12][13] introduce a hybrid model, which utilizes user-based similarity, POI-based (Point-Of-Interest) similarity, and geographic information to recommend tourist spots. Zheng et al [14] designed a hybrid trustbased model.…”

“…f in F then (6) Rinde x i + � w (f j ) * delement j . S { }; (7) end if (8) end for (9) if (Rinde x i ≥ λ) then (10) dn i ⟶ send positiveLibrary; (11) end if (12) if (Rinde x i ≤ c) then (13) dn i ⟶ send negativeLibrary; Mobile Information Systems composed of linear cells. We use the method of random gradient descent [31] to update the model parameters, and the generated word embedding is used to measure the semantic similarity of the input keyword.…”