Although many existing movie recommender systems have investigated recommendation based on information such as clicks and tags, much less efforts have been made to explore the multimedia content of movies, which has p...Although many existing movie recommender systems have investigated recommendation based on information such as clicks and tags, much less efforts have been made to explore the multimedia content of movies, which has potential information for the elicitation of the user＇s visual and musical preferences. In this paper, we explore the content from three media types （image, text, audio） and propose a novel multi-view semi-supervised movie recommendation method, which represents each media type as a view space for movies. The three views of movies are integrated to predict the rating values under the multi-view framework. ~rthermore, our method considers the casual users who rate limited movies. The algorithm enriches the user profile with a semi-supervised way when there are only few rating histories. Experiments indicate that the multimedia content analysis reveals the user＇s profile in a more comprehensive way. Different media types can be a complement to each other for movie recommendation. And the experimental results validate that our semi-supervised method can effectively enrich the user profile for recommendation with limited rating history.展开更多
In this paper we deal with Twitter and the presence of the keyword “Macedonia” in tweets over a period of time. We searched for the same term in three different languages, i.e. “Μακεδονíα”, “Macedoni...In this paper we deal with Twitter and the presence of the keyword “Macedonia” in tweets over a period of time. We searched for the same term in three different languages, i.e. “Μακεδονíα”, “Macedonia” and “Македонска - Македониjа”, since we are primarily interested in views from Greece and FYROM without excluding views from other regions. We use methods from Social Network Analysis (SNA) in order to create networks of users, calculate some main network metrics, measure user importance and investigate the presence of possible fragmentations—communities among them. We furthermore proceed to a form of content analysis, using pairs of words within tweets, in order to obtain main ideas, trends and public views that circulated over the network.展开更多
基金Supported by the National Basic Research 973 Program of China under Grant No. 2011CB302200-Gthe Key Program of National Natural Science Foundation of China under Grant No. 61033007+1 种基金the National Natural Science Foundation of China under Grant Nos.61100026, 60973019the Fundamental Research Funds for the Central Universities of China under Grant Nos. N110604003,N100704001, N100304004, N120404007
文摘Although many existing movie recommender systems have investigated recommendation based on information such as clicks and tags, much less efforts have been made to explore the multimedia content of movies, which has potential information for the elicitation of the user＇s visual and musical preferences. In this paper, we explore the content from three media types （image, text, audio） and propose a novel multi-view semi-supervised movie recommendation method, which represents each media type as a view space for movies. The three views of movies are integrated to predict the rating values under the multi-view framework. ~rthermore, our method considers the casual users who rate limited movies. The algorithm enriches the user profile with a semi-supervised way when there are only few rating histories. Experiments indicate that the multimedia content analysis reveals the user＇s profile in a more comprehensive way. Different media types can be a complement to each other for movie recommendation. And the experimental results validate that our semi-supervised method can effectively enrich the user profile for recommendation with limited rating history.
文摘In this paper we deal with Twitter and the presence of the keyword “Macedonia” in tweets over a period of time. We searched for the same term in three different languages, i.e. “Μακεδονíα”, “Macedonia” and “Македонска - Македониjа”, since we are primarily interested in views from Greece and FYROM without excluding views from other regions. We use methods from Social Network Analysis (SNA) in order to create networks of users, calculate some main network metrics, measure user importance and investigate the presence of possible fragmentations—communities among them. We furthermore proceed to a form of content analysis, using pairs of words within tweets, in order to obtain main ideas, trends and public views that circulated over the network.
