‎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

Establishing Knowledge Networks via Analysis of Research Abstracts

The extraction and propagation of knowledge inherent in a social network environment is demanding higher significance in research. The knowledge hidden within a social network would be easier to be comprehended if provided in a collective form. In the field of scientific research, such presentation of appreciated knowledge evolved from research communities would aid researchers. In this paper, we propose the evolution of a knowledge network from the information available in digital bibliographic repositories like DBLP [DBLP]. The most important characteristic of this knowledge network would be the comprehension of the proficiency of the scientist in the perspective of an area of research. This is achieved by categorizing the research articles published by an author into specific domains. The quality of the research articles are ascertained by analysing the abstracts within the domain. This analysis is used to determine the quality of the research article in terms of originality, relevancy and thereby, the impact of the article with respect to a research area. This quality measure provides knowledge on the impact of the scientist on the research community is arrived at as a cumulative entity. This knowledge helps in the evolution of the knowledge network from the social network of a research community