Matrix Factorization Techniques for Recommender Systems

On the truck dispatching problem

Poly (ionic liquid)s attracted enormous attention as adsorbents for the CO2 separation from natural gas. Hence, in this work poly (1-butyl-3-vinylimidazolium bromide), P[VBIm][Br], poly (1-butyl-3-vinylimidazolium thiocyanate), P[VBIm][SCN], and poly (1-butyl-3-vinylimidazolium tetrafluoroborate), P[VBIm][BF4] were synthesized and evaluated their CO2 adsorption performance. The synthesized poly(ionic liquid)s were characterized by 1H NMR, FT-IR spectroscopy, and differential scanning calorimetery (DSC) analysis. The CO2 adsorption was studied at various temperatures and pressures by quartz crystal microbalance (QCM) and experimental data were correlated by a model of dual-mode. The Henry and Langmuir contributions in CO2 adsorption were evaluated. The obtained thermodynamics and kinetics parameters of CO2 adsorption reveal that CO2 adsorption has an exothermic and physisorption nature. Also, density functional theory (DFT) computations were done in order to assess interactions between poly(ionic liquid)s with CO2 gas. DFT computations corroborated that interaction of P[VBIm][SCN] with CO2 is stronger than those of other poly(ionic liquid)s.

经Co

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Digital Video And Hdtv Algorithms And Interfaces

A mobile robot based system for fully automated thermal 3D mapping

Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user's preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user's preferences, but also group user's preferences affecting prediction of ratings for individual users. In this work is presented improved user-based collaborative filtering algorithm using temporal contextual information. With difference to other approaches, we propose using weight function based on changes in the group user's preferences over time that increases prediction accuracy of collaborative filtering prediction algorithm.

An approach to design of time-aware recommender system based on changes in group user's preferences

Using contextual information in recommender systems is a subject of continuous improvement of rating prediction accuracy. Among others, information on temporal rating dynamics contain valuable data that establish foundation for discovering changes in both individual and group user's preferences. Such changes can be caused by multiple factors such as changes of individual user interests, changes in item popularity or other hidden patterns or events. In this paper an improved user-based collaborative filtering algorithm is presented that utilizes changes of group user's preferences over time. We also investigate temporal dynamics of changes in user's preferences within different item categories and propose time weight function that improves prediction accuracy of recommender systems.

Temporal dynamics of changes in group user's preferences in recommender systems

In a traditional teaching models students are passive learners and usually share a common learning path that is in most cases predefined and fixed. In this paper we describe the case-based model for personalized learning path recommendation in example-based learning activities. According to the proposed model, the primary source of the knowledge for recommendation is represented in the form of cases that represent previously experienced learning paths. A recommended learning path comprises appropriate learning examples and learning resources for a student to go through in order to achieve desired learning outcomes. To validate the effectiveness of the proposed model, we conducted an experimentation to evaluate the impact of the model to student learning progress.

Case-Based Reasoning Model for Personalized Learning Path Recommendation in Example-Based Learning Activities

Color histograms are one of the earliest and best known image features used in Content-Based Image Retrieval (CBIR). There is a wealth of scientific work on this topic. However, different papers vary in the specific ways of determining histograms and distance between them. In this paper authors attempt to classify various types of histograms used in literature and compare them using contemporary datasets and metrics for evaluation. Histograms are compared based on their retrieval performance as well as resource usage.

https://ieeexplore.ieee.org/iel7/6588635/6596208/06596413.pdf

Comparative study of color histograms as global feature for Image Retrieval

A case-based reasoning (CBR) system that continuously interacts with an environment must be able to autonomously create new situation cases based on its perception of the local environment in order to select the appropriate steps to achieve the current mission goal Although many continuous problem domains seem appropriate for case-based reasoning, a general formal framework is still missing This paper presents a step in the direction of developing such a formal model of autonomous creation of new situation cases The model is based on the notion of the step for attentional shift This notion allows us to define the representation scheme for situation cases We have introduced two types of situation cases: contextual cases and action cases The solution component of contextual cases, also called a contextual behavior routine, is used as a resource to direct the attention of the CBR system to the relevant aspects of the local environment The solution component of action cases, also called an action behavior routine, is used to guide selection of manipulative steps There are two key roles of steps for attentional shift in our model The first one is that steps for attentional shift represent a description structure of situation cases The second role is that steps for attentional shift represent an abstract representation of actions by which the CBR system moves the attention to the relevant aspects of a local environment

Haris Supic

Papers

An approach to design of time-aware recommender system based on changes in group user's preferences

Temporal dynamics of changes in group user's preferences in recommender systems

Case-Based Reasoning Model for Personalized Learning Path Recommendation in Example-Based Learning Activities

Comparative study of color histograms as global feature for Image Retrieval

Autonomous creation of new situation cases in structured continuous domains