Over the last decade, there has been a burgeoning of data due to social media, e-commerce and overall digitisation of enterprises. The data is exploited to make informed choices, predict marketplace trends and patterns in consumer preferences. Recommendation systems have become ubiquitous after the penetration of internet services among the masses. The idea is to make use of filtering and clustering techniques to suggest items of interest to users. For a media commodity like movies, suggestions are made to users by finding user profiles of individuals with similar tastes. Initially, user preference is obtained by letting them rate movies of their choice. Upon usage, the recommender system will be able to understand the user better and suggest movies that are more likely to be rated higher. The experiment results on the MovieLens dataset provides a reliable model which is precise and generates more personalised movie recommendations compared to other models.

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.