Beyond Preference Prediction: Explaining Social Media Recommendations in Context
Date Issued
2009
Date
2009
Author(s)
Wu, David Kuan-Chun
Abstract
Historically the information overload problem is addressed with approaches rooted in recommender systems research, that is, functional prediction of user preference using ad hoc heuristics or probabilistic models. These approaches have been shown toe effective to a certain extent, but they fail to address different needs required by users when dealing with social media recommendation systems. These are systems in which content is shared and discussed among community users in the form of social recommendations. On these systems, presentation of only the most preferencematching items may hinder user interaction and demote user satisfaction. This thesis contributes by offering a different perspective into tackling the information overload problem on social media recommendation systems. Instead of preference prediction, we propose an approach to annotating social media recommendations with meaningful explanations. These explanations provide some context to assist the user effectively and efficiently decide which recommendations are of interest. Our experimental results empirically demonstrate that such contextual explanations not only harvest user’s trust in the system but also promote overall user satisfaction. This thesis also contributes by providing our results as a set of benchmarks for comparisons with future research.
Subjects
Social Media Recommendation System
Social Recommendations
Contextual Explanations
Information Overload Problem
Social Media Sharing
Type
thesis
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