The impact of multi-criteria ratings in social networking sites on the performance of online recommendation agents

Recommender Systems (RSs) have played an important role in online retailing portals and cus-tomers' decision-making processes. Recommender systems that are based on the conventional Collaborative Filtering (CF) approach rely on single customers' ratings on retailing websites. Multi-criteria CF (MCCF) approaches that rely on multi-aspects of the products have provided more reliable and effective recommendations on retailing websites. However, these approaches should be improved in terms of accuracy by solving sparsity issues and incorporating criteria ratings. In addition, most of the recommendation agents that are based on MCCF cannot learn automatically from the features of the products to model customers' preferences and generate accurate recommendations on retailing websites. Besides, although previous studies have utilized single and multi-criteria ratings in recommendation agents of tourism websites, still, if there is a lack of ratings of items, most of these systems will fail to generate accurate recommendations to users. In this research, we develop a new recommendation agent based on a MCCF approach to effectively improve the performance of previous recommendation systems for tourism websites. The results demonstrated that the method can predict the most relevant products to users, particularly when the dataset is sparse.

Automated summary

Recommender systems play a vital role in online retailing and customer decision-making. Conventional Collaborative Filtering (CF) approaches rely on individual customer ratings, while Multi-criteria CF (MCCF) approaches provide more reliable and effective recommendations on retailing websites. However, these approaches need improvement in terms of accuracy, addressing sparsity issues, and incorporating criteria ratings.