Using Heterogeneous Social Media as Auxiliary Information to Improve Hotel Recommendation Performance
نویسندگان
چکیده
منابع مشابه
Personalized Hotel Recommendation based on Social Networks
Recommender systems have become an important tool for users to identify interesting items as well as for businesses to promote their products to the right users. With the rapid development of social networks, travelers start to seek recommendations and advises from websites like TripAdvisor and Yelp. While travelers are willing to share their opinions on social networks, this provides an opport...
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Online user-generated content in various social media websites, such as consumer experiences, user feedback, and product reviews, has increasingly become the primary information source for both consumers and businesses. In this study, we aim to look beyond the quantitative summary and unidimensional interpretation of online user reviews to provide a more comprehensive view of online user-genera...
متن کاملSocial Media Recommendation
Social media recommendation is foreseen to be one of the most important services to recommend personalized contents to users in online social network. It imposes great challenge due to the dynamical behavior of users and the large-scale volumes of contents generated by the users. In this chapter, we first present the principal concept of social media recommendation. Then we present the framewor...
متن کاملRecommendation as Classi cation: Using Social and Content-Based Information in Recommendation
Recommendation systems make suggestions about artifacts to a user. For instance, they may predict whether a user would be interested in seeing a particular movie. Social recomendation methods collect ratings of artifacts from many individuals and use nearest-neighbor techniques to make recommendations to a user concerning new artifacts. However, these methods do not use the signi cant amount of...
متن کاملRecommendation as Classification: Using Social and Content-Based Information in Recommendation
Recommendation systems make suggestions about artifacts to a user. For instance, they may predict whether a user would be interested in seeing a particular movie. Social recomendation methods collect ratings of artifacts from many individuals, and use nearest-neighbor techniques to make recommendations to a user concerning new artifacts. However, these methods do not use the significant amount ...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2018
ISSN: 2169-3536
DOI: 10.1109/access.2018.2855690