Knowledge based topic retrieval for recommendations and tourism promotions

Abstract

The Users regularly share their views on services through internet reviews. Digital tourism allows travelers to plan and manage their trips ahead of time using the internet. The proposed system will enable users to create recommender systems that can help to find new places as per individual interests. The portals such as Tripadvisor and Yelp assist travelers in decision-making and providing feedback. The data available in the form of reviews, blogs, and rating help the researcher build a better recommendation and promotion system. The recommendation system will help tourists discover restaurants often ignored due to a lack of promotion. This research proposes an automated system to perform three subtasks: predicting star ratings from reviews, a feedback model, and a knowledge-based recommender system. The experimental work results show that Random Forest and Decision Tree classifiers give the highest accuracy for predicting star ratings. The research utilizes clustering and topic modeling to identify the topic from the reviews. Moreover, this research uses salience and valence of the topic to generate the feedback model. Furthermore, a recommender system is developed using user preferences, restaurant knowledge, and the valence of each topic of each restaurant. Inverse Document Frequency and K-means clustering have been used to improve the recommendation quality and construct a CSR matrix. This system will link restaurants with similar reviews to ensure that customers find restaurants almost identical to the ones they enjoyed in the past.

Keywords

• Tourism
• Online reviews
• Machine learning
• Recommender system
• Predicting ratings
• Feedback model