A Transfer Probabilistic Collective Factorization Model to Handle Sparse Data in Collaborative Filtering

2014 ◽  
Author(s):  
How Jing ◽  
An-Chun Liang ◽  
Shou-De Lin ◽  
Yu Tsao
Keyword(s):  
Collaborative Filtering ◽  
Sparse Data ◽  
Factorization Model

Industrialization Research and Realization of Preprocessing in Rough Set Based on Similar Prediction

2012 ◽  
Vol 251 ◽  
pp. 185-190
Author(s):  
Dun Hong Yao ◽  
Xiao Ning Peng ◽  
Jia He
Keyword(s):  
Data Processing ◽  
Collaborative Filtering ◽  
Rough Set ◽  
Data Preprocessing ◽  
Sparse Data ◽  
Data Evaluation ◽  
Prediction Algorithm ◽  
Rating Prediction ◽  
Essential Problem ◽  
Data Processing Methods

In every field which needs data processing, the sparseness of data is an essential problem that should be resolved, especially in movies, shopping sites. The users with the same commodity preferences makes the data evaluation valuable. Otherwise, without any evaluation of information, it will result in sparse distribution of the entire data obtained. This article introduces a collaborative filtering technology used in sparse data processing methods - project-based rating prediction algorithm, and extends it to the areas of rough set, the sparse information table processing, rough set data preprocessing sparse issues.

Personalisation of Advertisements in the Digital TV Context

2011 ◽  
pp. 264-284 ◽  
Author(s):  
George D. Lekakos ◽  
George M. Giaglis
Keyword(s):  
Collaborative Filtering ◽  
Hybrid Approach ◽  
Sparse Data ◽  
Digital Tv ◽  
Prediction Methods ◽  
Inherent Limitation ◽  
Rapid Changes ◽  
Filtering Technique ◽  
Stable Characteristic ◽  
Unique Domain

In this chapter, we discuss personalisation of advertisements in the digital TV environment and propose an effective personalisation approach, taking into account unique domain requirements. The proposed approach combines the widely used Pearson-based collaborative filtering technique, applied on numerical ratings with the user’s lifestyle, a stable characteristic drawn from consumer behaviour theory. We claim that users with similar lifestyles are reliable neighbours and can be utilised for the recommendation of advertisements for any member of their lifestyle neighbourhood. We focus on an inherent limitation of collaborative filtering methods that occurs when few ratings are available for each user and demonstrate that the proposed approach effectively manages this problem. Indeed, the hybrid approach combines the ability of the Pearson-based approach to accommodate rapid changes in user needs and make predictions upon one-click interactions and the advantage of the lifestyle-based approach to handle sparse data, which significantly affects the performance of collaborative filtering prediction methods.

scholarly journals Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey

2015 ◽  
Vol 49 ◽  
pp. 136-146 ◽  
Author(s):  
Dheeraj Bokde ◽  
Sheetal Girase ◽  
Debajyoti Mukhopadhyay
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Filtering Algorithms ◽  
Factorization Model

A new generalized collaborative filtering approach on sparse data by extracting high confidence relations between users

2021 ◽  
Author(s):  
Mohsen Ramezani ◽  
Fardin Akhlaghian Tab ◽  
Alireza Abdollahpouri ◽  
Mahmud Abdulla Mohammad
Keyword(s):  
Collaborative Filtering ◽  
Sparse Data ◽  
High Confidence ◽  
Filtering Approach

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

2015 ◽  
Vol 82 ◽  
pp. 163-177 ◽  
Author(s):  
Bidyut Kr. Patra ◽  
Raimo Launonen ◽  
Ville Ollikainen ◽  
Sukumar Nandi
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Sparse Data ◽  
Bhattacharyya Coefficient

A new item similarity based on α-divergence for collaborative filtering in sparse data

2021 ◽  
Vol 166 ◽  
pp. 114074
Author(s):  
Yong Wang ◽  
Pengyu Wang ◽  
Zhuo Liu ◽  
Leo Yu Zhang
Keyword(s):  
Collaborative Filtering ◽  
Sparse Data ◽  
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