Abstract-Recommender systems (RSs) provide personalised suggestions of information or products relevant to users' needs. Although RSs have made substantial progresses in theory and algorithm development and have achieved many commercial successes, how to utilise the widely available information in Online Social Networks (OSNs) has been largely overlooked. Noticing such a gap in the existing research in RSs and taking into account a user's selection being greatly influenced by his/her trusted friends and their opinions, this paper proposes a framework of Implicit Social Trust and Sentiment (ISTS) based RSs, which improves the existing recommendation approaches by exploring a new source of data from friends' short posts in microbloggings as micro-reviews. The impact degree of friends' sentiment and level being trusted to a user's selection are identified by using machine learning methods including Naive Bayes, Logistic Regression and Decision Trees. As the verification of the proposed framework, experiments using real social data from Twitter microblogger are presented and results show the effectiveness and promising of the proposed approach.
Online reviews have become the major driving factor influencing purchasing behavior and patterns of social customers. However, it is difficult for customer to cover good reviews about any product or service according to massive amount of reviews latest years. Many previous researches provide innovative models about predicting review helpfulness in E-commerce websites. Some of these studies exploring the direct effect of review attributes on review helpfulness while others focused on reviewer's attributes only. The main objective of this research is to review the most important attributes that have an affect on review helpfulness from many perspectives such as datasets, techniques, frameworks and evaluation methods of the experiments. The paper ends up with important findings about most attributes effect the review helpfulness such as Review Valence.
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