![]() ![]() In: Proceedings of the 8th International Conference on Semantic System, I-SEMANTICS’12. J Web Semant 7:154–165ĭi Noia T, Mirizzi R, Ostuni VC, Romito D, Zanker M (2012) Linked open data to support content-based recommender systems. In this investigation, the system analyzes user feedbacks to evaluate the recommendation accuracy through metrics of precision, recall and F-score rates, while cold-start users make use the system with two MovieLens datasets as main rating reference in the recommendation system.īizer C, Lehmann L, Kobilarov G, Auer S, Becker C, Cyganiak R, Hellmann S (2009) Dbpedia– a crystallization point for the web of data. ![]() Knowing that user rating data processing is a large-scale problem in producing high quality recommendations, MapReduce and NoSQL environments are employed in performing efficient similarity measurement algorithms whilst maintaining rating and film datasets. In order to solve cold-start problems, Cluster-based Matrix Factorization is adopted to model user implicit ratings related to Apps usage. Among those films recommended, users can give ratings and feedback, collecting film information from linked data concurrently. ![]() Given that mobile Apps are rapidly growing, the recommender is implemented to support web services in frontend Apps. Once you and your friends are all set up in the system, you can select whoever you're watching a movie with in that moment (whether it's one person or a group of people) and the app will use its algorithm to suggest films that you and your selected users will most likely share an interest in.Movie recommendation systems are important tools that suggest films with respect to users’ choices through item-based collaborative filter algorithms, and have shown positive effect on the provider’s revenue. When your friends join too, your account will be automatically linked with theirs thanks to the app's social media integration, so your interests in films can easily be combined to receive movie recommendations that appeal to everyone. So, how does it work? After linking Blend to your social media accounts (Facebook, Twitter, anything) and filling out your user profile - giving the a complete idea of your movie preferences - your data is ready for blending. And even better, it launches on Wednesday, so you don't have to wait at all to find it in the App store! This all-too-relatable conundrum inspired the Avi and Joshua Stern, creators of the personalized movie recommendation app MovieGrade, to create a new group-minded function called Blend, which exists for the sole purpose of helping you and your friends find a movie to watch based on your combined interests, and all of your previously rated films put together. The struggle is real, and if you're a film fanatic, you know it all too well. When you throw someone else in to the equation like a partner, or - gasp, even a group of friends - finding a movie you all want to watch can end up taking so much time, you might even decide against watching a movie altogether. Trying to pick a movie to watch even when you're alone can take so long that, by the time you choose, you probably realize you could have watched an entire movie in the time you were browsing for one. ![]()
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