content-based filtering

A Recommendation system is a BI tool that it uses data mining methods for guiding and helping the user to select the best items based on her/his preferences and in the shortest time. Despite more than two decades of academic research on recommendation systems, this issue is still one of the most up-to-date research challenges. Recommendation systems save the users time, increase their satisfaction and their loyalty to sales sites and lead to the development of e-commerce, by personalizing the recommendations of goods or services to site users. Nowadays, recommendation systems have many applications in various sectors of e-commerce, especially in media products such as books, movies, and music. The famous e-commerce sites such as eBay, Amazon, and Netflix and domestic sites such as Digikala, Divar, and Filimo widely use recommendation systems. These systems use a variety of big data filtering methods to provide appropriate recommendations. The most important and widely used filtering method is collaborative filtering (CF). In this paper, we implement three CF recommender systems based on the correlation coefficient between users, selecting the optimal number of neighbors and calculating weighted scores for unwatched movies. The best method with the least error is selected as the desired model. We use Movielens ml-latest-small 100k research dataset with 9742 movies and 610 users as input. The results showed 3.29% less RMSE error compared with the latest research that has used the correlation method.

The growing amount of information on the internet has made it difficult for many web users to make the decision-making and selection of information, data or goods. Recommended systems are designed to address this challenge and try to offer specific and useful information with respect to user tastes and past user experiences. So far, many offering systems have been developed in a variety of applications including movies, music, books, hotels etc. Choosing the right trip, the hotel proposal and so on, with regard to the individual's budget usually have a lot of difficulties and concerns for users and generally takes a lot of time and energy. In this paper, a travel and hotel recommendation system is developed which is constructed from combination of different filtering methods to maximize accuracy. The system is considering the current user's preferences, the quality of the service packages and past experiences of the same users with the current user in order to providing more accurate suggestions. It also eliminates the cold start problem.