‎provenance based trust boosted recommender system using boosted vector similarity measure

‎as users in an online social network are overwhelmed by the abundant amount of information‎, ‎it is very hard to retrieve the preferred or required content‎. ‎in this context‎, ‎an online recommender system helps to filter and recommend content such as people,items or services‎. ‎but‎, ‎in a real scenario‎, ‎people rely more on recommendations‎ ‎from trusted sources than distrusting sources‎. ‎though‎, ‎there are many trust based recommender systems that exist‎, ‎it lag in prediction error‎. ‎in order to improve the accuracy of the prediction‎, ‎this paper proposes a trust-boosted recommender system (tbrs)‎. ‎since‎, ‎the provenance derives the trust in a better way than other approaches‎, ‎tbrs is built‎ ‎from the provenance concept‎. ‎the proposed recommender system takes the provenance based fuzzy rules which were derived from the fuzzy decision tree‎. ‎tbrs then computes the multi-attribute vector similarity score and boosts the score with trust weight‎. ‎this system is tested on the book-review dataset to recommend the top-k trustworthy reviewers.the performance of the proposed method is evaluated in terms of mae and rmse‎. ‎the result shows that the error value of boosted similarity is lesser than without boost‎. ‎the reduced error rates of the jaccard‎, ‎dice and cosine similarity measures are 18%‎, ‎15% and 7% respectively‎. ‎also‎, ‎when the model is subjected to failure analysis‎, ‎it gives better performance for unskewed data than slewed data‎. ‎the models fbest‎, ‎average and worst case predictions are 90%‎, ‎50% and <23% respectively‎.