scholarly journals CONSTRUCTION OF PATTERNS OF USER PREFERENCES DYNAMICS FOR EXPLANATIONS IN THE RECOMMENDER SYSTEM

2021 ◽  
Vol 5 (1) ◽  
pp. 107-112
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi
Keyword(s):  
Recommendation System ◽  
Intelligent System ◽  
Process Mining ◽  
Consumer Preferences ◽  
System Development ◽  
Temporal Patterns ◽  
User Preferences ◽  
Graph Representation ◽  
Time Interval ◽  
The Subject

The subject of study in the article is the processes of constructing explanations in recommendation systems. Objectives. The goal is to develop a method of constructing patterns that reflect the dynamics of user preferences and provide an opportunity to form an explanation of the recommended list of items, taking into account changes in the user’s requirements of the recommendation system. Construction of explanations taking into account the dynamics of changes in consumer preferences makes it possible to increase user confidence in the results of the intelligent system. Tasks: structuring models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system; development of a method for constructing patterns of changing user preferences using process mining technology; experimental verification of the method for constructing patterns of changing consumer preferences. The approaches used are: temporal logics, which determine the approaches to the description of the temporal ordering of a set of events. The following results are obtained. The structuring of models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system is performed; developed and performed an experimental test of the method of constructing patterns of user preferences dynamics. Conclusions. The scientific novelty of the results is as follows. The method of dynamics patterns construction of users’ preferences for the formation of explanations concerning the recommended list of subjects is offered. The method sequentially generates a set of ordered events, each of which reflects the choice of the subject by a group of users at a certain time interval, and also builds a graph representation of the patterns of user preferences through intellectual analysis of processes. The patterns obtained as a result of the method consist of time-ordered pairs of events that reflect the knowledge of changing user preferences over time. Further use of such dependencies as elements of the knowledge base makes it possible based on probabilistic inference to build a set of alternative explanations for the received recommendation, and then arrange these explanations according to the probability of their implementation for the recommended list of subjects.

scholarly journals Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

2020 ◽  
Vol 2 (95) ◽  
pp. 21-27
Author(s):  
S. F. Chalyi ◽  
V. O. Leshchynskyi
Keyword(s):  
Recommendation System ◽  
Selection Process ◽  
Temporal Patterns ◽  
User Preferences ◽  
Data Sets ◽  
Goods And Services ◽  
Temporal Relationships ◽  
Sequential Selection ◽  
System User ◽  
Alternative Choice

The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

scholarly journals METHOD OF CONSTRUCTING EXPLANATIONS FOR RECOMMENDER SYSTEMS BASED ON THE TEMPORAL DYNAMICS OF USER PREFERENCES

2020 ◽  
Vol 3 ◽  
pp. 43-50
Author(s):  
Serhii Chalyi ◽  
Volodymyr Leshchynskyi
Keyword(s):  
Recommendation System ◽  
Temporal Dynamics ◽  
Numerical Estimate ◽  
User Preferences ◽  
Time Intervals ◽  
Active Selection ◽  
System A ◽  
Time Stamps ◽  
The Subject ◽  
Selection Of

The problem of constructing explanations for recommendations in situations of cold start and shilling attacks is considered. The first situation is characterized by incomplete information about the user's preferences, and the second is characterized by a distortion of the ratings of items in the recommendation system. A method for constructing explanations for the recommended list of subjects is proposed. The method uses weighted temporal dependencies to form explanations. Each such dependence reflects a change in sales of goods for two non-contiguous time intervals. These intervals are set according to a given level of detail of time, for example, day, week, month. The input is presented by a sales journal with time stamps. The method includes the steps of forming temporal rules, calculating the weights of the rules, building explanations. The weights of the rules reflect the degree of change in sales for a pair of intervals. The result of the method is a recommendation in the form of a numerical estimate of the change in user preferences with respect to the subject in the recommendation. The proposed method allows to increase sales efficiency due to the active selection of items by the user based on the explanations received

scholarly journals Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms

2020 ◽  
Vol 19 (3) ◽  
pp. 1635
Author(s):  
Basim Amer Jaafar ◽  
Methaq Talib Gaata ◽  
Mahdi Nsaif Jasim
Keyword(s):  
Recommendation System ◽  
Intelligent System ◽  
Clustering Algorithms ◽  
Weather Conditions ◽  
User Preferences ◽  
New Combination ◽  
Number Of Clusters ◽  
Weather Information ◽  
Electrical Devices ◽  
Climatic Characteristics

<p>The recommendation system is an intelligent system gives recommendations to users to discover the best interesting items. The purpose of this proposed recommendation system is to develop a system to find the best electrical devices according to weather conditions and user preferences. The proposed solution relies on the characteristics of electrical appliances and their suitability to weather conditions in any city. The proposed solution is the first recommendation system combines devices properties, weather conditions, and user preferences using a new combination of algorithms. The clustering algorithms are the most applicable in the field of recommendation system. The proposed solution relies on a combination of Elbow method, pro­­posed modified K-means and Silhouette algorithm to find the best number of clusters before starting the clustering process. Then calculate the weights for each cluster and compare them with the weather weights to find the required clusters sorted from the near to far according to a computed threshold. The empirical results showed that the proposed solution demonstrated a 94% accuracy to match the characteristics of the recommended devices with the climatic characteristics of the region and user preferences. The accuracy is measured using Silhouette algorithm.</p>

Improved Cluster-Minkowski Recommendation System (ICRS) for Collaborative Filtering

2019 ◽  
Vol 16 (9) ◽  
pp. 3892-3896
Author(s):  
Bhavana ◽  
Neeraj Raheja
Keyword(s):  
Comparative Analysis ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation System ◽  
Intelligent System ◽  
User Preferences ◽  
User Profiles ◽  
Content Filtering ◽  
History Of ◽  
Clustering Approach

Recommendation systems are intelligent system which provides suggestion according to user adaptability. Recommender systems i.e., collaborative filtering and content filtering works on the basis of user profiles, extensive history of user preferences and item descriptions. This paper proposes an improved recommendation system based on clustering approach. The comparative analysis shows that the proposed system provides better results in terms of RMSE as compared to other already existing methods.

An Adaptive and Learning System for Feedback-Controlled Evoked Response Studies

1981 ◽  
Vol 20 (03) ◽  
pp. 169-173
Author(s):  
J. Wagner ◽  
G. Pfurtscheixer
Keyword(s):  
Brain Activity ◽  
Evoked Response ◽  
Learning System ◽  
Time Interval ◽  
Electrical Brain Activity ◽  
Long Time ◽  
The Subject ◽  
Eeg Power ◽  
Stimulation System ◽  
Background Eeg

The shape, latency and amplitude of changes in electrical brain activity related to a stimulus (Evoked Potential) depend both on the stimulus parameters and on the background EEG at the time of stimulation. An adaptive, learnable stimulation system is introduced, whereby the subject is stimulated (e.g. with light), whenever the EEG power is subthreshold and minimal. Additionally, the system is conceived in such a way that a certain number of stimuli could be given within a particular time interval. Related to this time criterion, the threshold specific for each subject is calculated at the beginning of the experiment (preprocessing) and adapted to the EEG power during the processing mode because of long-time fluctuations and trends in the EEG. The process of adaptation is directed by a table which contains the necessary correction numbers for the threshold. Experiences of the stimulation system are reflected in an automatic correction of this table. Because the corrected and improved table is stored after each experiment and is used as the starting table for the next experiment, the system >learns<. The system introduced here can be used both for evoked response studies and for alpha-feedback experiments.

scholarly journals Amount and location of tectonic uplift in the Urmia region of northwest Iran from the Permian to the Neogene

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Akram Alizadeh
Keyword(s):  
Hypersaline Lake ◽  
Time Interval ◽  
Lake Basin ◽  
Lake Urmia ◽  
Qom Formation ◽  
Northwest Of Iran ◽  
The Subject ◽  
Orogenic Uplift ◽  
East Azerbaijan ◽  
Urmia Lake Basin

AbstractThe Urmia Lake Basin is located between the West and East Azerbaijan provinces in the northwest of Iran. Lake Urmia is the twentieth largest lake and second largest hypersaline lake in the world. Stratigraphic columns have been constructed, using published information, to compare the sedimentary units deposited from the Permian to the Neogene on the east and west sides of the lake, and to use these to quantity subsidence and uplift. East of the lake, the sedimentary section is more complete and has been the subject of detailed stratigraphic studies, including the compilation of measured sections for some units. West of the lake, the section is incomplete and less work has been done; three columns illustrate variations in the preserved stratigraphy for the time interval. In all cases, the columns are capped by the Oligocene–Miocene Qom Formation, which was deposited during a post-orogenic marine transgression and unconformably overlies units ranging from Precambrian to Cretaceous. Permian to Cretaceous stratigraphy is used to measure subsidence in the Lake Urmia basin up to the end of the Cretaceous, and then, the subsequent orogenic uplift, which was followed by further subsidence recorded by the deposition of the Qom Formation in the Oligocene–Miocene.

scholarly journals A scoping review of chronotype and temporal patterns of eating of adults: Tools used, findings, and future directions

2021 ◽  
pp. 1-51
Author(s):  
Yan Yin Phoi ◽  
Michelle Rogers ◽  
Maxine P. Bonham ◽  
Jillian Dorrian ◽  
Alison M. Coates
Keyword(s):  
Energy Intake ◽  
Scoping Review ◽  
Assessment Tool ◽  
Temporal Patterns ◽  
Time Interval ◽  
Dark Cycle ◽  
Future Directions ◽  
Meal Skipping ◽  
Optimal Health ◽  
The Difference

Abstract Circadian rhythms, metabolic processes, and dietary intake are inextricably linked. Timing of food intake is a modifiable temporal cue for the circadian system and may be influenced by numerous factors, including individual chronotype—an indicator of an individual’s circadian rhythm in relation to the light-dark cycle. This scoping review examines temporal patterns of eating across chronotypes and assesses tools that have been used to collect data on temporal patterns of eating and chronotype. A systematic search identified thirty-six studies in which aspects of temporal patterns of eating including meal timings; meal skipping; energy distribution across the day; meal frequency; time interval between meals, or meals and wake/sleep times; midpoint of food/energy intake; meal regularity; and duration of eating window were presented in relation to chronotype. Findings indicate that compared to morning chronotypes, evening chronotypes tend to skip meals more frequently, have later mealtimes, and distribute greater energy intake towards later times of the day. More studies should explore the difference in meal regularity and duration of eating window amongst chronotypes. Currently, tools used in collecting data on chronotype and temporal patterns of eating are varied, limiting the direct comparison of findings between studies. Development of a standardised assessment tool will allow future studies to confidently compare findings to inform the development and assessment of guidelines that provide recommendations on temporal patterns of eating for optimal health.

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