HYPERTEXT BROWSING: A NEW MODEL FOR INFORMATION FILTERING BASED ON USER PROFILES AND DATA CLUSTERING

1996 ◽  
Vol 20 (1) ◽  
pp. 3-10
Author(s):  
Bracha Shapira ◽  
Peretz Shoval ◽  
Adi Raveh ◽  
Uri Hanani
Keyword(s):  
Data Clustering ◽  
Information Filtering ◽  
User Profiles ◽  
New Model

Fuzzy Clustering of Web User Profiles for Analyzing their Behavior and Interests

2012 ◽  
pp. 91-114
Author(s):  
Stanislav Kreuzer ◽  
Natascha Hoebel
Keyword(s):  
Data Mining ◽  
Consumer Behavior ◽  
Fuzzy Clustering ◽  
Data Clustering ◽  
Ordinal Data ◽  
Customer Relationships ◽  
User Profiles ◽  
Data Mining Techniques

One of the keys to building effective e-customer relationships is an understanding of consumer behavior online. However, analyzing the behavior of customers online is not necessarily an indicator of their interests. Therefore, building profiles of registered users of a website is of importance if it goes beyond collecting obvious information the user is willing to give at the time of the registration. These user profiles can contribute to the analysis of the users’ interests. Important tools for the analysis are data-mining techniques, for example, the clustering of collected user information. This chapter addresses the problem of how to define, calculate, and visualize fuzzy clusters of Web visitors with respect to their behavior and supposed interests. This chapter shows how to cluster Web users based on their profile and by their similar interests in several topics using the fuzzy and hybrid CORD (Clustering of Ordinal Data) clustering system, which is part of the Gugubarra Framework.

scholarly journals A Preference Model on Adaptive Affinity Propagation

2018 ◽  
Vol 8 (3) ◽  
pp. 1805 ◽  
Author(s):  
Rina Refianti ◽  
Achmad Benny Mutiara ◽  
Asep Juarna ◽  
Adang Suhendra
Keyword(s):  
Data Clustering ◽  
Message Passing ◽  
Clustering Algorithms ◽  
Experimental Results ◽  
Data Sets ◽  
Similarity Matrix ◽  
Preference Model ◽  
New Model ◽  
Data Points ◽  
Scanning Algorithm

In recent years, two new data clustering algorithms have been proposed. One of them isAffinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.

INFORMATION FILTERING BASED ON USERS' NEGATIVE OPINIONS

2013 ◽  
Vol 24 (05) ◽  
pp. 1350032 ◽  
Author(s):  
QIANG GUO ◽  
YANG LI ◽  
JIAN-GUO LIU
Keyword(s):  
Heat Conduction ◽  
Information Filtering ◽  
Similarity Measures ◽  
Information Model ◽  
Experimental Results ◽  
Collective Behaviors ◽  
New Model ◽  
Model Based ◽  
Ranking Score ◽  
High Diversity

The process of heat conduction (HC) has recently found application in the information filtering [Zhang et al., Phys. Rev. Lett.99, 154301 (2007)], which is of high diversity but low accuracy. The classical HC model predicts users' potential interested objects based on their interesting objects regardless to the negative opinions. In terms of the users' rating scores, we present an improved user-based HC (UHC) information model by taking into account users' positive and negative opinions. Firstly, the objects rated by users are divided into positive and negative categories, then the predicted interesting and dislike object lists are generated by the UHC model. Finally, the recommendation lists are constructed by filtering out the dislike objects from the interesting lists. By implementing the new model based on nine similarity measures, the experimental results for MovieLens and Netflix datasets show that the new model considering negative opinions could greatly enhance the accuracy, measured by the average ranking score, from 0.049 to 0.036 for Netflix and from 0.1025 to 0.0570 for Movielens dataset, reduced by 26.53% and 44.39%, respectively. Since users prefer to give positive ratings rather than negative ones, the negative opinions contain much more information than the positive ones, the negative opinions, therefore, are very important for understanding users' online collective behaviors and improving the performance of HC model.

scholarly journals A Prerecognition Model for Hot Topic Discovery Based on Microblogging Data

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Tongyu Zhu ◽  
Jianjun Yu
Keyword(s):  
Life Cycle ◽  
Information Sharing ◽  
Data Clustering ◽  
Topic Detection ◽  
New Model ◽  
Topic Detection And Tracking ◽  
Topic Discovery ◽  
Detection And Tracking ◽  
Emerging Topics

The microblogging is prevailing since its easy and anonymous information sharing at Internet, which also brings the issue of dispersing negative topics, or even rumors. Many researchers have focused on how to find and trace emerging topics for analysis. When adopting topic detection and tracking techniques to find hot topics with streamed microblogging data, it will meet obstacles like streamed microblogging data clustering, topic hotness definition, and emerging hot topic discovery. This paper schemes a novel prerecognition model for hot topic discovery. In this model, the concepts of the topic life cycle, the hot velocity, and the hot acceleration are promoted to calculate the change of topic hotness, which aims to discover those emerging hot topics before they boost and break out. Our experiments show that this new model would help to discover potential hot topics efficiently and achieve considerable performance.

The Hitachi Model HS-8 Electron Microscope

1967 ◽  
Vol 25 ◽  
pp. 238-239
Author(s):  
H. Akabori ◽  
K. Nishiwaki ◽  
K. Yoneta
Keyword(s):  
Electron Microscope ◽  
New Model ◽  
Predecessor Model

By improving the predecessor Model HS- 7 electron microscope for the purpose of easier operation, we have recently completed new Model HS-8 electron microscope featuring higher performance and ease of operation.

516: A Simplified Method to Estimate the Absolute Renal Uptake of 99MTC-DMSA: Evaluation with a New Model using the Radioactivity of Nephrectomy Specimens as Reference

2005 ◽  
Vol 173 (4S) ◽  
pp. 140-141
Author(s):  
Mariana Lima ◽  
Celso D. Ramos ◽  
Sérgio Q. Brunetto ◽  
Marcelo Lopes de Lima ◽  
Carla R.M. Sansana ◽  
...  
Keyword(s):  
New Model ◽  
Renal Uptake ◽  
Simplified Method ◽  
The Absolute
Sign in / Sign up

Export Citation Format

Share Document