![]() None of this makes sense.”ĭisney’s Star Wars adopted it, to some degree ( they fly now!), and there’s a new Dungeons & Dragons movie coming out, that, from the trailer, looks far more obnoxious than Forspoken. The city is flying, we’re fighting an army of robots and I have a bow and arrow. For example, in the height of an intense battle during Age of Ultron, Hawkeye says: "Okay, look. The results showed that our method is effective in improving the sparsity and scalability problems in CF.It isn’t always horrible - this kind of humor works for a character like Iron Man - but the MCU has used this style excessively, often to its detriment. We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature. In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition (SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scal-ability of the recommendation method. Then, we use ontology to improve the accuracy of recommendations in CF part. ![]() Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques. In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches. It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time. Improving the efficiency of methods has been a big challenge in recommender systems.
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