A novel recommendation algorithm based on product life cycle theory

A large number of practical applications of the recommendation system found that the novelty of the recommendation results and the user satisfaction are more closely related, making the novelty recommendation recently widely concerned and studied. Many novelty recommendation algorithms used the popularity of the item to measure novelty, but this method is too simple, and the change of item popularity is more reflective of its novelty. According to the product life cycle theory (PLC), this study proposed a novelty recommendation algorithm that recommends item that be not popular now and may be popular in the future to improve the novelty of the recommendation results, The time change of the popularity of the items to be recommended is analyzed, and the future popularity of the items are predicted by analogy. Two strategies for selecting recommended selection are selecting future popular items (the predicting popularity-based filtering Algorithm, PP algorithm) and excluding future recession items (the Excluding Recession-based filtering algorithm, ER algorithm), according to the definition of novelty of the item, recommended the novelty items to the target user. The effectiveness of the proposed algorithm was verified through an offline experiment. Results indicate that PP algorithm can significantly improve the accuracy and novelty, but seriously sacrifice the coverage and reduce the ability of the recommendation system to mine the long tail items when the number of alternative items N is small, the novelty of the recommendation list of the ER algorithm is remarkably higher than that of traditional algorithms, the novelty is high when the quantity of alternative sets reaches around 350, where the average popularity of the recommendation list declines by 40%, and the coverage is elevated by 150%, thereby improving the ability of the proposed system to extract all kinds of items. This study serves as reference for the improvement of user satisfaction with recommendation systems.

Fault Diagnosis Method Based on Improved Evidence Reasoning

Evidence reasoning (ER) combined with dimensionless index method can be used in rotating machinery fault diagnosis. In ER algorithm, reliability is mainly obtained in two ways: distance-based method and correlation measure by set theory. In practice, the distance-based method cannot generate high-discrimination reliability in high-coincidence data like dimensionless index data. Therefore, correlation measure by set theory method is used in fault diagnosis more frequently. Because correlation measure by set theory only considers upper bound and lower bound of fault data, we add a regularization term to calculate the relationship between the inner data. Experience result shows that fault diagnosis accuracy had improved, which illustrates that the new reliability can describe data relationship better.

An Improved Grey Target Approach for Technology Contribution Analysis Using the Evidential Reasoning Algorithm

An improved Grey Target (GT) approach using the Evidential Reasoning (ER) algorithm, named as ER-GT, is proposed to handle the challenge when conducting the contribution analysis in the Weapon System of Systems (WSoS) context. The WSoS technology contribution analysis is a small-sample, less-data problem without a recognized pattern, which fits the characteristics of the problems that GT is designed to solve. However, the standard system in GT may not exist at all since its indices are derived from different systems and fails to be consistent with the System Military Value (SMV), which represents the contribution made of a system to WSoS. In order to make certain adjustments of the standard system, the information derived from capabilities requirements, practical restraints and/or experts’ knowledge must be taken into consideration. Since the ER algorithm has a privilege in dealing with different types of information under uncertainty, it is applied to transform the standard system into a “mission system” so as to integrate different types of information. A comparative case using both GT and ER-GT is studied to validate the efficiency of the proposed ER-GT approach. The study result shows that the defense capability is given a bigger contribution rate, which has been neglected by traditional GT.

Context-Based Entity Resolution

Prior work of entity resolution involves expensive similarity comparison and clustering approaches. Additionally, the quality of entity resolution may be low due to insufficient information. To address these problems, by adopting context information of data objects, the authors present a novel framework of entity resolution, Context-Based Entity Description (CED), to make context information help entity resolution. In this framework, each entity is described by a set of CEDs. During entity resolution, objects are only compared with CEDs to determine its corresponding entity. Additionally, the authors propose efficient algorithms for CED discovery, maintenance, and CED-based entity resolution. The authors experimentally evaluated the CED-based ER algorithm on the real DBLP datasets, and the experimental results show that this algorithm can achieve both high precision and recall as well as outperform existing methods.

Applied Research on ER Algorithm of Gray Modeling

To improve the precision of gray modeling in forest fire and solve the problem of small date modeling, ER algorithm is proposed. Based on the senior introduced the robust estimation to gray modeling, this method interpolate the modeling date again. The method can achieve small date (3 dates) modeling. This research compared with three calculation methods: least squares method, least squares interpolation method and ER algorithm. According to the fitting precision, least squares method is 10.21%, least squares interpolation method is 1.08% and ER algorithm is 0.00%. That can be obtained by calculating ER algorithm has a good fitting effect.

A Subjective Risk-Estimation Approach for Modeling Ship Hull Vibration

A subjective novel approach incorporating an evidential reasoning (ER) algorithm is developed to achieve the risk estimation of ship hull vibration (SHV). A hierarchical structure for SHV modeling (hazard identification model) is constructed using a combined qualitative and quantitative approach. The quantitative criteria are converted to the qualitative criteria by applying a rule-based quantitative data transformation technique to make use of ER. A mapping process is formulated to convert and quantify the qualitative criteria. Intelligent Decision System (IDS) software is used for synthesis in the hierarchical structure and to produce the risk-estimation results graphically. The results of this paper reveal that the ER is capable of producing the risk estimation of SHV.