Ogglas Ogglas ToList ; friends. AddRange ReceievedFriendRequests. OnModelCreating modelBuilder ; modelBuilder.
RequestedById ; modelBuilder. This is an excellent solution. Have you encountered any issue with modelling like this since?
This model has been accused of Photoshopping her friend to look bigger in their pictures
Mukus Did not try it out with that many users but yes the basics worked. Yes you can absolutely do that, just you need to disable lazy loading feature by setting false value to LazyLoadingEnabled, context. Mukesh Mukesh 2 2 silver badges 11 11 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook.
Jack LaLanne – A Hero And A Friend
Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. Featured on Meta.
A Temporal-Topic Model for Friend Recommendations in Chinese Microblogging Systems Abstract: Due to its brief form and growing popularity, microblogging is becoming people's favorite choice for seeking information and expressing opinions. Messages received by a user mainly depend on whom the user follows. Thus, recommending users with similar interests may improve the experience quality for information receiving.
- Conviction of the Innocent: Lessons From Psychology Research!
- The Seven Ordeals of Count Cagliostro?
- Street Hypnosis?
- Meet Taylor Swift's friend posse of models and actresses - Business Insider!
Since messages posted by microblogging users reflect their interests, and the keywords in the messages indicate their main focus to a large extent, we can discover users' preferences by analyzing the user-generated contents. Moreover, users' interests are not static, on the contrary, they change as time goes by. Based on such intuitions, in this paper, we propose a temporal-topic model to analyze users' possible behaviors and predict their potential friends in microblogging.
The model learns users' latent preferences by extracting keywords on aggregated messages over a period of time via a topic model, and then the impact of time is considered to deal with interest drifts. The experimental results of friend recommendations on Sina Weibo, one of the most popular microblogging sites in China, have demonstrated the effectiveness of our model.
Article :. Date of Publication: 28 January