Smart Tourism Recommender System Modeling Based on Hybrid Technique and Content Boosted Collaborative Filtering

• Published in: IEEE access Vol. 12; pp. 131794 - 131808
• Main Authors: Huda, Choirul, Heryadi, Yaya, Lukas, Budiharto, Widodo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Business; Collaboration; Collaborative filtering; Cultural differences; Customization; Data mining; Demographics; Demography; Error analysis; Filtering; Filtration; Matrix reduction; Modelling; Performance evaluation; recommender system; Recommender systems; Root-mean-square errors; Sentiment analysis; Sparse matrices; Synthetic data; Tourism; Tourism industry
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3450882

A recommender system (RecSys) is a smart solution that offers personalized items to users. Utilizing adequate information from historical transactions in the tourism industry such as items, users, ratings, and reviews becomes valuable input in providing personalized RecSys regarding smart decision-making for users in tourism. This study proposes a new approach for tourism RecSys development through a hybrid model combining User-Based Collaborative Filtering (UBCF), Demographic Filtering (DF), Aspect-Based Sentiment Analysis (ABSA), and Content-Boosted Collaborative Filtering (CBCF). This model utilizes six steps in the Cross-Industry Standard Process for Data Mining (CRISP-DM): business understanding, data understanding, data preparation, modeling, evaluation, and deployment. A tourism dataset has been produced by combining raw data from the TripAdvisor website with demographic information from Google Maps to enhance user profiles. The model performance was evaluated using the measurement of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) through the 5 cycles of 10-Fold Cross Validation. Based on the average results of MAE and RMSE, CBCF has achieved performance improvements respectively at 84.7% and 82.3% compared to the performance of UBCF using 100% dense UI-Matrix. This study has succeeded in proposing a novelty model for tourism recommender systems in avoiding the cold-start problem and sparse matrix reduction through the synthetic data generation regarding performance improvement of the recommender system complemented with related aspects of recommended attractions.