Matrix factorization (MF) has evolved as one of the better practice to handle sparse data in field of recommender systems. Funk singular value decomposition (SVD) is a variant of MF that exists as state-of-the-art method that enabled winning the Netflix prize competition. The method is widely used with modifications in present day research in field of recommender systems. With the potential of data points to grow at very high velocity, it is prudent to devise newer methods that can handle such data accurately as well as efficiently than Funk-SVD in the context of recommender system. In view of the growing data points, I propose a latent factor model that caters to both accuracy and efficiency by reducing the number of latent features of either users or items making it less complex than Funk-SVD, where latent features of both users and items are equal and often larger. A comprehensive empirical evaluation of accuracy on two publicly available, amazon and ml-100 k datasets reveals the comparable accuracy and lesser complexity of proposed methods than Funk-SVD.

/pdf/a-novel-latent-factor-model-for-recommender-system-1f2f0jv2we.pdf

A novel latent factor model for recommender system

This paper studies how to deploy relay nodes into traditional wireless sensor networks with constraint aiming to simultaneously optimize two important factors; average energy consumption and average network reliability. We consider tackling this multi-objective (MO) optimization problem with three metaheuristics, which employ greatly different evolutional strategies, and aim at an in-depth analysis of different performances of these metaheuristics to our problem. For this purpose, a statistical procedure is employed to analyse the results for confidence, in consideration of two MO quality metrics; hypervolume and coverage of two sets. After comprehensive analysis of the results, it is concluded that NSGA-II provides the best performance.

A Multi-Objective Metaheuristics Study on Solving Constrained Relay Node Deployment Problem in WSNS

Web log mining is mainly to acquire users’ interest navigation patterns from web logs and has been the subject of the web personalization research. In this paper, we define a new concept “interest pheromone” and present a group users’ navigation paths model. Then we propose a simple algorithm based on improved Ant Colony Optimization (ACO) to mine users’ dynamic interest. In this algorithm, three factors relative browsing time, access frequency and operation time are considered to measure the “interest pheromone”, which better reflects users’ real interest. Finally, we conduct the simulation experiments to contrast the accuracy of navigation patterns mined by our approach and existing approaches. Experimental results illustrate that the proposed paradigm can truly capture users’ browsing preference effectively.

/pdf/mining-users-interest-navigation-patterns-using-improved-ant-2sxdg57sfn.pdf

Mining Users Interest Navigation Patterns Using Improved Ant Colony Optimization

Context preserving navigation redesign under Markovian assumption for responsive websites

Improved Teaching Learning Based Optimization and Its Application in Parameter Estimation of Solar Cell Models

In recent years, as one of the most successful recommendation methods, collaborative filtering has been widely used in the recommendation system. Collaborative filtering predicts the active user preference for goods or services by collecting a historical data set of users' ratings for items; the underlying assumption is that the active user will prefer those items that the similar users prefer. Usually the data is quit sparse, which makes the computation of similarity between users or items imprecise and consequently reduces the accuracy of recommendations. In this paper, we propose an enhanced similarity method that the common ratings and the all ratings are both taken into account. Additionally, we present a generative probabilistic prediction framework in which we first predict a missing data probability value interval instead of a certain value by using the defined range of similar neighbors' ratings, and the final missing data rating is produced in the interval. Empirical studies on two datasets (Mov...

An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction

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Mining Users Interest Navigation Patterns Using Improved Ant Colony Optimization

An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction