research-article

Collaborative Filtering Recommendation Algorithm Based on Social Relation and Geographic Information

Authors:

Kelu LiAuthors Info & Claims

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

Article No.: 14, Pages 1 - 7

https://doi.org/10.1145/3207677.3278022

Published: 22 October 2018 Publication History

Abstract

Collaborative1 filtering algorithm has such problems as sparsity of raw data, low efficiency and accuracy of recommendation. To solve the problems, this paper proposed the collaborative filtering algorithm based on complementary conditions of social relationship and geographic information. The algorithm firstly introduced the social relation data into the matrix complementation process, which reduces the sparseness of the original user item scoring matrix and enhances the authenticity of the complement data; then used the user's geographic information to filter the information that is used to complement matrix, which drops the error of complementing data and improves the accuracy of complementing data; finally, selected complement items conditionally, which increases the recommendation efficiency and recommendation accuracy of the algorithm remarkably. The algorithm is verified through experiments, and the experimental results prove that the improved algorithm is correct and effective in solving the problem of low original data sparseness and low recommendation accuracy.

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Cited By

Chaudhari ASeddig ASarlan ARaut R(2022)A Comprehensive Study on Recommendation Engines2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA10.1109/ICCUBEA54992.2022.10011001(1-9)Online publication date: 26-Aug-2022
https://doi.org/10.1109/ICCUBEA54992.2022.10011001
Margaris DSpiliotopoulos DVassilakis C(2021)Identifying Reliable Recommenders in Users’ Collaborating Filtering and Social NeighbourhoodsBig Data and Social Media Analytics10.1007/978-3-030-67044-3_3(51-76)Online publication date: 6-Jul-2021
https://doi.org/10.1007/978-3-030-67044-3_3
Margaris DSpiliotopoulos DVassilakis CAlhajj R(2020)Neighbourhood aging factors for limited information social network collaborative filteringProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381314(877-883)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/ASONAM49781.2020.9381314

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cover image ACM Other conferences

CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering

October 2018

1083 pages

ISBN:9781450365123

DOI:10.1145/3207677

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zhihong Qian

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 October 2018

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CSAE '18

CSAE '18: The 2nd International Conference on Computer Science and Application Engineering

October 22 - 24, 2018

Hohhot, China

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CSAE '18 Paper Acceptance Rate 189 of 383 submissions, 49%;

Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

Chaudhari ASeddig ASarlan ARaut R(2022)A Comprehensive Study on Recommendation Engines2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA10.1109/ICCUBEA54992.2022.10011001(1-9)Online publication date: 26-Aug-2022
https://doi.org/10.1109/ICCUBEA54992.2022.10011001
Margaris DSpiliotopoulos DVassilakis C(2021)Identifying Reliable Recommenders in Users’ Collaborating Filtering and Social NeighbourhoodsBig Data and Social Media Analytics10.1007/978-3-030-67044-3_3(51-76)Online publication date: 6-Jul-2021
https://doi.org/10.1007/978-3-030-67044-3_3
Margaris DSpiliotopoulos DVassilakis CAlhajj R(2020)Neighbourhood aging factors for limited information social network collaborative filteringProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381314(877-883)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/ASONAM49781.2020.9381314

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