2018
DOI: 10.1007/978-3-319-77712-2_12
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Sentiment Analysis of Social Network Data for Cold-Start Relief in Recommender Systems

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Cited by 32 publications
(28 citation statements)
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“…In fact, this problem has received a considerable amount of attention in another context, the context of recommender systems that seek to predict the rating that a user would give in e-commerce or online streaming websites to an item based on his or her interest (e.g., books, movies, songs). Many studies (e.g., Menon et al, 2011;Forsati et al, 2014;Lika, Kolomvatsos, & Hadjiefthymiades, 2014;Ling, Lyu, & King, 2014;Pereira & Hruschka, 2015;Barjasteh et al, 2016;Fernández-Tobías et al 2016;Contratres et al, 2018) proposed data mining and machine learning techniques (specifically, collaborative filtering algorithms) to address the cold-start problem using the side information about existing users (i.e., users' attributes) to make recommendations for new users with similar profiles. However, most of their approaches focus heavily on the prediction of the new user's rates on a given set of items, lacking the psychometric component i.e., assessment of the users' latent traits.…”
Section: Introductionmentioning
confidence: 99%
“…In fact, this problem has received a considerable amount of attention in another context, the context of recommender systems that seek to predict the rating that a user would give in e-commerce or online streaming websites to an item based on his or her interest (e.g., books, movies, songs). Many studies (e.g., Menon et al, 2011;Forsati et al, 2014;Lika, Kolomvatsos, & Hadjiefthymiades, 2014;Ling, Lyu, & King, 2014;Pereira & Hruschka, 2015;Barjasteh et al, 2016;Fernández-Tobías et al 2016;Contratres et al, 2018) proposed data mining and machine learning techniques (specifically, collaborative filtering algorithms) to address the cold-start problem using the side information about existing users (i.e., users' attributes) to make recommendations for new users with similar profiles. However, most of their approaches focus heavily on the prediction of the new user's rates on a given set of items, lacking the psychometric component i.e., assessment of the users' latent traits.…”
Section: Introductionmentioning
confidence: 99%
“…They mixed the analysis of social networks using the semantic concepts and the analysis of social graphs by integrating the processing of semantic knowledge. Also, the Recommender System in [12] employs textual data from different social network platforms such as Twitter or Facebook to recommend specific items for new users. By collecting users' posts, comparing them with their relevant products, and generate recommendations according to the collected data.…”
Section: Related Workmentioning
confidence: 99%
“…Musto et al [26] designed a multi-criteria collaborative filtering method, which uses aspect-based sentiment analyses of users' reviews to obtain sentiment scores as ratings of items from users. Contratres et al [27] proposed a recommendation process that includes sentiment analysis to textual data extracted from Facebook and Twitter and presented the results of an experiment in which this algorithm was used to reduce the cold start issue. Seo et al [28] proposed a friendship strength-based personalized recommender system.…”
Section: Related Workmentioning
confidence: 99%