Applying Artificial Intelligence Techniques for Devising Recommendation System

Recommendation System filters the information from large data source by recommending or predicting the interest of the user. This have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. The most popular domains where recommendation is done are movies, music, search queries, social tags, and products in general. However, there are also recommender systems for experts, jokes, restaurants, financial services, life insurance and Twitter followers. Our project mainly focuses on domain called Food. Where, food can be recommended either based on text mining or by using artificial neural networks. In text mining, the user writes the query and recommendation is done on the basis of that given query. In artificial neural networks, recommendation is done by entering area or dish. In this project use of artificial neural networks as the core prediction function of a recommender system. In the past, ANNs have mainly concentrated on using collaborative-based filtering.