An intelligent personalized recommendation for travel group planning based on reviews
Chinnaiah Valliyammai, Ramachandran Prasannavenkatesh, C Vennila, Siddharth Krishnan · 2017
Recommender Systems are information filtering systems that guide the users in selecting the desired items based on the past user-item transactions. Recommender Systems have become the vital role in recent years and are utilized widely in various areas of social importance. The proposed work aims in recommending the most suitable touring facilities that include customized places recommender and formation of travel groups that help user in choosing the places to visit based on the other users with similar interests. The system utilizes a model based collaborative filtering to predict the requirements of items for future. The conventional cold start and data sparsity problems are addressed in the proposed system. The system incorporates the element of trust in the traditional system and takes the advantages of the fuzzy c-means algorithm and Apriori algorithm in data classification. The proposed system is shown to provide a high degree of personalized recommendation.