# Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions
###### Abstract  
We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making. multi-task framework; tensor factorization; sensors; pre-training; feature engineering; collaborative recommendation  
as a result of the cold start problem of users and items [6; 7]. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education and other domains with more recent successful developments of recommender systems for real-world applications. According to [8], Multi-task learning on the other hand, is an approach designed to improve predictive performance by learning from multiple related tasks simultaneously, taking into account the relationships and information shared among the different tasks [9]. The multi-task learning paradigm activates a great deal of model interpretability and it is able to provide explicit information for further analysis on user behavior patterns according to current research in information filtering [10]. In this paper, we focus on explicit feedback, through user's auxiliary data which directly depicts users' preference through ratings and sentiments expressed [11]. A radial basis function [12] is used to filter the complex interactions between the items, users and sentiments scores of users of an e-commerce site. Compared to implicit feedback, this paper explores user information in more than two dimensions and treats it as a tensor model. Deep neural network model for the existing relationships is then formulated in a Multilayer Perceptron (MPL) model. Regarding the key content filtering effectiveness, MF when used to fuse item and user characteristics with inner product sometimes hinders better performance as a result of the cross product normally counted as cost. This paper mitigates the just mentioned research constraints with a multi-modal data and models it as a multi-task algorithm, which is hybrid in real sense. Therefore, a hybrid recommendation based on multi-task learning framework for solving cold start and scalability problems is proposed for collaborative filtering in this paper. The main contributions of this paper are:  
1. A novel tensor factorization strategy, which adapts radial basis of Gaussian type to solve the cold start problem is proposed. 2. A hybrid collaborative system using the speed of the multilayer perceptron and the simplicity and accuracy of Tensor Factorization to produce fast and accurate model for improved performance to ensure a scalable processing of big data is proposed. 3.