Impact of Multimedia in Sina Weibo

Multimedia contents such as images and videos are widely used in social network sites nowadays. Sina Weibo, a Chinese microblogging service, is one of the first microblog platforms to incorporate multimedia content sharing features. This thesis provides statistical analysis on how multimedia contents are produced, consumed, and propagated in Sina Weibo. Based on 230 million tweets and 1.8 million user profiles in Sina Weibo, we study the impact of multimedia contents on the popularity of both users and tweets as well as tweet life span. In addition to consider the multimedia impact on popularity, we also compare the user influence in multimedia and text setting. Our preliminary study shows that multimedia tweets dominant pure text ones in Sina Weibo. Multimedia contents boost popularity of tweet as well as users. Users who tend to publish many multimedia tweets are also productive with text tweet. We prove that tweets with multimedia contents survive longer than text tweets. Finally, multimedia contents tend to attract more attention while text maintains discussion. Our results demonstrates the impact of multimedia in Sina Weibo with respect to how it affects the popularity, life span of tweets and the popularity of user. Our result is useful for web developers and microblogging marketers

Modelling user behaviour for Web recommendation using LDA model

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analysis of Web usage data will lead to discover Web user access pattern and facilitate users locate more preferable Web pages via collaborative recommending technique. Meanwhile, latent semantic analysis techniques provide a powerful means to capture user access pattern and associated task space. In this paper, we propose a collaborative Web recommendation framework, which employs Latent Dirichlet Allocation (LDA) to model underlying topic-simplex space and discover the associations between user sessions and multiple topics via probability inference. Experiments conducted on real Website usage dataset show that this approach can achieve better recommendation accuracy in comparison to existing techniques. The discovered topic-simplex expression can also provide a better interpretation of user navigational preference. © 2008 IEEE

Design and Implementation of a Customer Personalised Recomender System

[ANGLÈS] Market basket analysis is examined through the application of probabilistic topic models and case-based reasoning in order to provide more insight into customer buying habits and generate meaningful recommendations