Paper Title
Multi-Criteria Recommender System Using Collaborative Filtering and Aspect Based Sentiment Analysis for User Reviews of Food Recipes

In the past few years, a significant growth has been seen in the technologies that surround the concept of Information Filtering. Recommender systems is a subclass of the same and it came into play around 1990 when they were mentioned as a "digital bookshelf" by Jussi Karlgen in a technical report. Through this paper, we aim to ideate a working scenario for a Multi-criteria Recommender System (MCRS) that combines Collaborative Filtering (CF) and Aspect-Based Sentiment Analysis (ABSA) to recommend food recipes based on the aspects mined from the user reviews. Our system utilizes CF to identify similar users and recommend recipes based on what their previous likes and preferences have been. We further incorporate ABSA to comprehend user reviews as it analyzes consumer review data by correlating sentiments to different aspects and subaspects of the recipe review. These techniques are applied on a dataset that contains food recipes along with their ratings and textual reviews. We target to judge the conditions in which the recommender system works best by determining the Mean Average Error (MAE) in each scenario. Whichever condition produces the least MAE is the ideal scenario for a recommender system to work in. Keywords - Information Filtering, Recommendation Systems, Collaborative Filtering, Aspect Based Sentiment Analysis.