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One of the most important and challenging problems in recommendation systems is that of modeling temporal behavior. Typically, modeling temporal behavior increases the cost of parameter inference and estimation. Along with it, it also poses the constraint of requiring a large amount of data for reliably learning the parameters of the model. Therefore, it is often difficult to model temporal behavior...
As the most widely used recommendation algorithm, collaborative filtering (CF) has been studied for many years due to its simplicity and effectiveness. The two main categories of CF have their own shortcomings. Memory-based CF can't generate accurate results when faced with data sparsity; and model-based CF always loses the information between users or items. To alleviate this problem, we propose...
With the rapid growth of Web 2.0, social media has become a prevalent information sharing and spreading platform, where users can retweet interesting messages. To better understand the propagation mechanism for information diffusion, it is necessary to model the user retweeting behavior and predict future retweets. Some existing work in retweeting prediction based on matrix factorization focuses on...
Conventional pedestrian detection methods construct models based on hand-crafted features or deep learning. They are powerful but limited due to finite capabilities of single classifiers. Ensemble models escape these problems by assembling multiple classifiers using some man-made criteria which synthetically utilize information from all combined models. However, these criteria lack theoretical support...
Big data and cloud computing are the two top IT initiatives that are in the mind for industries across the globe. Both innovations keep on evolving. As a delivery model for IT services, cloud computing has the potential to enhance agility and productivity while enabling greater efficiencies and reducing costs. As a result a number of enterprises are building efficient and agile cloud environments,...
Nowadays, the explosive growth and variety of information available on the Web frequently overwhelms users and leads users to make poor decisions. Consequently, recommender systems have become more and more important to assist people to make decisions faster. Among all related techniques, collaborative filtering approach is currently one of the effective and widely used techniques to build recommender...
In recent years, the Internet of Things (IoT) has been an inseparable part of our lives. IoT is typically heterogeneous in nature and requires interconnection with different types of devices or “things”. Being able to secure such a distributed environment is an onerous task. The heterogeneity of IoT, along with other factors, poses a challenge when it comes to securing communication between these...
The graph-based algorithm for personalized recommendations mainly depends on the user-item model to construct a bipartite graph. We can provide recommendations by analyzing the bipartite graphs. However, for personalized videos recommendations, the classical recommendation algorithm based on graphs has low efficiency. Therefore, this paper gives an improved video recommendation algorithm that is based...
In this paper we detail our initial approach and early results in examining the efficacy of a Markovbased stochastic model to course enrollment recommendations. We outline a Markov-based collaborative filtering model to recommend courses to students at each semester based on the sequence of courses they have taken in the previous semesters. The proposed model is based on the enrollment data and no...
In this work, we address the problem of transfer learning for sequential recommendation model. Most of the state-of-the-art recommendation systems consider user preference and give customized results to different users. However, for those users without enough data, personalized recommendation systems cannot infer their preferences well or rank items precisely. Recently, transfer learning techniques...
Though cloud computing technology is usually used to analyze big data, for business data produced by collaborative task system, the massive business data sets should be analyzed from a business process perspective, more than a technology perspective. To achieve the goal, an analysis approach based on iterative computation is proposed. Firstly, taking data sequence analysis in collaborative workflows...
The increasing growth of e-commerce industry in Indonesia motivates e-commerce sites to provide better services to its customer. One of the strategies to improves e-commerce services is by providing personal recommendation, which can be done using recommender systems. However, there is still lack of studies exploring the best technique to implement recommender systems for e-commerce in Indonesia....
This paper focus on building recommender system with weighted parallel hybrid method for e-commerce in Indonesia. The dataset was derived from one of the largest ecommerce company in Indonesia. The experiments used three sampling techniques, namely bootstrapping validation, timing series and systematic sampling. The best result of these experiments yields F1-measure of 9.99% which is achieved by the...
Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned...
Behavior-based recommendation algorithm is one class of the most important methods in recommendation systems. A variety of models are researched for a long time, such as collaborative filtering, graph-based models, matrix factorization and so on. Different characteristics in many aspects of these methods are also be analyzed. In this study, we design a new similarity measure from the perspective of...
Personalization recommendation can effectively solve the negative influence of information overload to users in the environment of big data. And the existing personalization recommendation model is insufficient in integrating the time characteristics of users' behavior. We build a new extend model of personalization recommendation based on the method of latent factor model. Time characteristics of...
Scholarly paper recommendation has been an important research topic in the field of information filtering because scholars find thousands of publications that match their search queries but are largely irrelevant to their latent information needs. Many existing methods are based on the users' online behaviours to construct user interest model. However, an author's published works constitute a clean...
User model is the key of recommendation algorithm, in order to solve the great sparsity of user-item rating model, proposed a collaborative filtering recommendation algorithm based on weighted item category. The algorithm evaluates the rated items by category, and with weighted scores summation, which changed the user-item high-dimensional rating data into user-category low-dimensional statistical...
Learning from Demonstration (LfD) is addressed in this work in order to establish a novel framework for Human-Robot Collaborative (HRC) task execution. In this context, a robotic system is trained to perform various actions by observing a human demonstrator. We formulate a latent representation of observed behaviors and associate this representation with the corresponding one for target robotic behaviors...
This paper examines the assembly of interdisciplinary teams in emerging scientific fields. We develop and validate a hybrid systems dynamics and agent-based computational model using data over a 15 year period from the assembly of teams in the emerging scientific field of Oncofertility. We found that, when a new field emerges, team assembly is influenced by the reputation and seniority of the researchers,...
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