ESLI: Enhancing slope one recommendation through local information embedding.
Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommenda...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0222702 |
| _version_ | 1818590780286042112 |
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| author | Heng-Ru Zhang Yuan-Yuan Ma Xin-Chao Yu Fan Min |
| author_facet | Heng-Ru Zhang Yuan-Yuan Ma Xin-Chao Yu Fan Min |
| author_sort | Heng-Ru Zhang |
| collection | DOAJ |
| description | Slope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error. |
| first_indexed | 2024-12-16T10:01:59Z |
| format | Article |
| id | doaj.art-3252b5538594453d9b5b0b14a2971daa |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-16T10:01:59Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-3252b5538594453d9b5b0b14a2971daa2022-12-21T22:35:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-011410e022270210.1371/journal.pone.0222702ESLI: Enhancing slope one recommendation through local information embedding.Heng-Ru ZhangYuan-Yuan MaXin-Chao YuFan MinSlope one is a popular recommendation algorithm due to its simplicity and high efficiency for sparse data. However, it often suffers from under-fitting since the global information of all relevant users/items are considered. In this paper, we propose a new scheme called enhanced slope one recommendation through local information embedding. First, we employ clustering algorithms to obtain the user clusters as well as item clusters to represent local information. Second, we predict ratings using the local information of users and items in the same cluster. The local information can detect strong localized associations shared within clusters. Third, we design different fusion approaches based on the local information embedding. In this way, both under-fitting and over-fitting problems are alleviated. Experiment results on the real datasets show that our approaches defeats slope one in terms of both mean absolute error and root mean square error.https://doi.org/10.1371/journal.pone.0222702 |
| spellingShingle | Heng-Ru Zhang Yuan-Yuan Ma Xin-Chao Yu Fan Min ESLI: Enhancing slope one recommendation through local information embedding. PLoS ONE |
| title | ESLI: Enhancing slope one recommendation through local information embedding. |
| title_full | ESLI: Enhancing slope one recommendation through local information embedding. |
| title_fullStr | ESLI: Enhancing slope one recommendation through local information embedding. |
| title_full_unstemmed | ESLI: Enhancing slope one recommendation through local information embedding. |
| title_short | ESLI: Enhancing slope one recommendation through local information embedding. |
| title_sort | esli enhancing slope one recommendation through local information embedding |
| url | https://doi.org/10.1371/journal.pone.0222702 |
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