Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system

Multi-criteria collaborative filtering using rough sets theory

Recommender systems have recently become a significant part of e-commerce applications. Through the different types of recommender systems, collaborative filtering is the most popular and successful recommender system for providing recommendations. Recent studies have shown that using multi-criteria ratings helps the system to know the customers better. However, bringing multi aspects to collaborative filtering causes new challenges such as scalability and sparsity. Additionally, revealing the relation between criteria is yet another optimization problem. Hence, increasing the accuracy in prediction is a challenge. In this paper, an aggregation-function based multi-criteria collaborative filtering system using Rough Sets Theory is proposed as a novel approach. Rough Sets Theory is used to uncover the relationship between the overall criterion and the individual criteria. Experimental results show that the proposed model (RoughMCCF) successfully improves the predictive accuracy without compromising on online performance.

Artificial intelligence in recommender systems

Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

An effective social recommendation method based on user reputation model and rating profile enhancement

Trust-aware recommender systems are advanced approaches which have been developed based on social information to provide relevant suggestions to users. These systems can alleviate cold start and data sparsity problems in recommendation methods through trust relations. However, the lack of sufficient trust information can reduce the efficiency of these methods. Moreover, diversity and novelty are important measures for providing more attractive suggestions to users. In this article, a reputation-based approach is proposed to improve trust-aware recommender systems by enhancing rating profiles of the users who have insufficient ratings and trust information. In particular, we use a user reliability measure to determine the effectiveness of the rating profiles and trust networks of users in predicting unseen items. Then, a novel user reputation model is introduced based on the combination of the rating profiles and trust networks. The main idea of the proposed method is to enhance the rating profiles of the users who have low user reliability measure by adding a number of virtual ratings. To this end, the proposed user reputation model is used to predict the virtual ratings. In addition, the diversity, novelty and reliability measures of items are considered in the proposed rating profile enhancement mechanism. Therefore, the proposed method can improve the recommender systems about the cold start and data sparsity problems and also the diversity, novelty and reliability measures. Experimental results based on three real-world datasets show that the proposed method achieves higher performance than other recommendation methods.

A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset

Products and web pages are the main components of the e-commerce data knowledge and the relationship among them is an important issue to be highly considered in recommender systems. This study aims to focus on the similarity and complementarity relationships among the products that have wide applications in the recommender systems. In the previously proposed methods, products and their relationships were revealed using taxonomy and “IS-A” relationship. In addition, the similarity and complementarity calculations were conducted based on edge computation by assigning a similar degree to any edge. More specifically, the children of a concept in the taxonomy was supported by a similar father’s “IS-A” degree. In contrast, this study provides a new approach based on ontology, data mining, and automatic discovering algorithms for the relationships with different degrees for the edges among the concepts. Accordingly, these relationships are initialised according to the “IS-A” degree. With regard to this weighted taxonomy, the semantic similarity and complementarity are measured based on concept distance. In addition, the proposed recommender system is item-based, which uses semantic similarity and complementarity. The required data for the present study were collected from construction materials supplier. The results illustrated that our proposed method is effective for construction materials recommendation.

A new hand-eye calibration approach for fracture reduction robot

OBJECTIVE

The hand-eye calibration is used to determine the transformation between the end-effector and the camera marker of the robot. But the robot movement in traditional method would be time-consuming, inaccurate and even unavailable in some conditions. The method presented in this article can complete the calibration without any movement and is more suitable in clinical applications.

METHODS

Instead of solving the classic non-linear equation AX = XB, we collected the points on X and Y axes of the tool coordinate system (TCS) with the visual probe and fitted them using the singular value decomposition algorithm (SVD). Then, the transformation was obtained with the data of the tool center point (TCP). A comparison test was conducted to verify the performance of the method.

RESULTS

The average translation error and orientation error of the new method are 0.12 ± 0.122 mm and 0.18 ± 0.112° respectively, while they are 0.357 ± 0.347 mm and 0.416 ± 0.234° correspondingly in the traditional method.

CONCLUSIONS

The high accuracy of the method indicates that it is a good candidate for medical robots, which usually need to work in a sterile environment.

Accuracy Improvement for Predicting Parkinson's Disease Progression

Parkinson's disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson's datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.