Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Model-Based CF Algorithm <s> Collaborative Filtering is widely used in the recommendation system which gives recommendation based on the similar liking users. The Collaborative Recommender System (CR) suffers from sparsity and cold-start problems. We design and implementation an intelligent kernel based Fuzzy Collaborative Recommender System for map user needs to products that can satisfy them based on ratings. In this paper we present an intelligent, smoothened, fuzzy kernel based approach to solving this problem. The system is intelligent, so the user gets recommendation based on fuzzy similarity with the neighbours. The approach is smoothened, so it uses radial basis function network to smooth the sparse rating matrix. In this manner, it allows the efficient determination of products that meet the user’s requirements. Experimental results based on the Movie lens dataset show that the proposed system only by smoothening provides more than 95% accuracy for existing users in the highly sparse data. The results proved that the proposed system outperforms existing systems such as Support Vector Machine, Multi Layer Perceptron, Bayesian, and Singular Value Decomposition, in terms of accuracy and relevance of recommendations. Finally the system is developed for our college library book application and it is tested by our college students. <s> BIB001 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> 2) Model-Based CF Algorithm <s> Mobile devices that are capable of playing Internet videos have become wide-spread in recent years. Because of the enormous offer of video content, the lack of sufficient presentation space on the screen, and the laborious navigation on mobile devices, the video consumption process becomes more complicated for the end-user. To handle this problem, people need new instruments to assist with the hunting, filtering and selection process. We developed a methodology for mobile devices that makes the huge content sources more manageable by creating a user profile and personalizing the offer. This paper reports the structure of the user profile, the user interaction mechanism, and the recommendation algorithm, an improved version of the Bayesian classifier that incorporates aspects of the consumption context (like time, location, and mood of the user) to make the suggestions more accurate. <s> BIB002
Model-based algorithms use the collection of ratings to learn and compute a model of a user, which is then used to make rating predictions. If a comparison is made between memory-based algorithms and model-based algorithms, memory-based algorithms can be considered "lazy learning" algorithms and methods because they do not build a model, but instead perform the heuristic computations when collaborative recommendations are requested. The design and development of models such as data mining and machine learning algorithms can allow the system to recognize complex patterns based on training data, and make intelligent predictions for the CF tasks for real-world data or test data based on the learned models. Model-based CF algorithms such as Bayesian Models, Clustering Models and Dependency Networks have been investigated to address the disadvantages of memory-based CF algorithms. In both memory-based and model-based approaches used in CF, the users are clustered based on the similarity. Similarity is calculated based on rating matrix, using Pearson correlation function or Cosine function.  Bayesian Classifier/Simple Bayesian CF Algorithm: The Bayesian classifier which is commonly used and applied in CF systems has been proved as an efficient classification mechanism in several fields. In principle the classifier bases on a 2-class decision model, where an item in a domain object belongs to the class like or dislike. Some researchers have recognized Bayesian classifier as an exceptionally well-performing model-based CF algorithm. For instance, Pessemier et al. BIB002 reported that the structure of the user profile, the user interaction mechanism, the recommendation Figure 5 .Workflow of hybrid mobile multimedia recommendation algorithm and an improved version of the Bayesian classifier that incorporates aspects of the consumption context (like time, location, and mood of the user) can make the suggestions and predictions more accurate. To this effect, they developed a methodology for mobile devices that makes the huge content sources more manageable by creating a user profile and personalizing the available resources.  Bayesian Belief Network CF Algorithm: A Bayesian Network (BN) is a Directed Acyclic Graph (DAG), where the nodes represent the variables from the problem that has to be solved. In the application of BN, the first task is to select those variables which are relevant to the problem being tackled. Each variable will be a node in the DAG and whenever two variables are related, a path must exist between them in the graph. These connections can be determined from an input dataset by means of a learning algorithm. The Bayesian network is commonly used for CF BIB001 .