Publication: From the social semantic web to recommendation
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2016-10
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Abstract
This paper present a new recommendation algorithm based on contextual analysis and new measurements. Social Network is one of the most popular Web 2.0 applications and related services, like Facebook, have evolved into a practical means for sharing opinions. Consequently, Social Network web sites have since become rich data sources for opinion mining. This paper proposes to introduce external resource from comments posted by users to predict recommendation and relieve the cold start problem. The novelty of the proposed approach is that posts are not simply characterized by an opinion score, as is the case with machine learning-based classifiers, but instead receive an opinion grade for each distinct notion in the post. Our approach has been implemented with Java and Lenskit framework; the study resulted in Movie dataset, we have shown positive results. We compared our algorithm to Slope One algorithm. We have obtained an improvement of 8% in precision and recall as well an improvement of 16% in RMSE and nDCG.
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Poster Papers of ISCRAM-Med 2016 - Third International Conference on Information Systems for Crisis Response and Management in Mediterranean Countries. October 26-28, 2016. Universidad Carlos III de Madrid (Spain)
Keywords
Recommendation system, Collaborative filtering, User profile, Social network, User cold start