Titlebook: Web and Big Data; First International Lei Chen,Christian S. Jensen,Xiang Lian Conference proceedings 2017 Springer International Publishin

GRUEL

N斯巴達人

lymphedema

繁殖

Boost Clickbait Detection Based on User Behavior Analysiscrease click-through rate, but decrease user experience. Thus, it is important to identify the articles with a misleading title and block them for specific users. Existing methods just consider text features, which hardly produce a satisfactory result. User behavior is useful in clickbait detection.

含糊其辭

A Novel Hybrid Friends Recommendation Framework for Twitter recently in the area of social networks analysis as users would like to follow people who have similar interests to them. We use Twitter as a case study and propose a novel hybrid friends recommendation framework that is not only based on friends relationship but also users’ location information, w

匍匐前進

superfluous

商店街

A Time and Sentiment Unification Model for Personalized Recommendationng items. Intuitively, users buying items online are influenced not only by their preferences and public attentions, but also by the crowd sentiment (i.e., the word of mouth) to the items. Specifically, users are likely to refuse an item whose most reviews are negative from the crowd. Therefore, a g

微不足道

mettlesome

Personalized POI Groups Recommendation in Location-Based Social Networksnsist of POIs. POI Groups have a significant impact on people’s lives and urban planning. Every person has her/his own personalized POI Groups (PPGs) based on preferences and friendship in location-based social networks (LBSNs). However, there are almost no researches on this aspect in recommendatio