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Making recommendations in Social Networks based on textual reviews: a confidence-based approach

Dionisis Margaris, Costas Vassilakis, Dimitris Spiliotopoulos

SoDa Technical report TR-19002

Note: a newer version of this report is available here.

Abstract:
In this technical report, we present the experimental findings from applying an algorithm that (1) considers the characteristics of Social Networks (SNs) user reviews which affect the review-to-rating conversion procedure, (2) computes a confidence level for each rating, which reflects the uncertainty level for each conversion process and (3) exploit this metric both in the users’ similarity computation and in the prediction formulation phases in recommender systems.
More specifically, we evaluate the performance of the proposed approach in terms of SN users’ satisfaction regarding the recommendations formulated based on the rating predictions generated by the proposed algorithm.

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Year: 
2019
Research area: 
Social networks
Recommender systems