Semantic-based service recommendation method on mapreduce using user-generated feedback

Abstract

Service recommender systems provide same recommendations to different users based on ratings and rankings only, without considering the preference of an individual user. These ratings are based on the single criteria of a service ignoring its multiple aspects. Big data also affects these recommender systems with issues like scalability and inefficiency. Proposed system enhances existing recommendations systems and generates recommendations based on the categorical preferences of the present user by matching them with the feedback/comments of the past users. System semantically analyzes the users feedback and distinguishes it into positive and negative preferences to eliminate the unnecessary reviews of the users which boosts the system accuracy. Approximate and exact similarity between the preferences of present and past users is computed and thus the recommendations are generated using SBSR algorithm. To improve the performance, i.e., scalability and efficiency in big data environment, SBSR is ported on distributed computing platform, Hadoop.