Aggregated Recommendation through Random Forests
Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation. In this paper, we propose a random forest approach to create...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/649596 |
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| _version_ | 1849396782796111872 |
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| author | Heng-Ru Zhang Fan Min Xu He |
| author_facet | Heng-Ru Zhang Fan Min Xu He |
| author_sort | Heng-Ru Zhang |
| collection | DOAJ |
| description | Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation. In this paper, we propose a random forest approach to create aggregated recommender systems. The approach is used to predict the rating of a group of users to a kind of items. In the preprocessing stage, we merge user, item, and rating information to construct an aggregated decision table, where rating information serves as the decision attribute. We also model the data conversion process corresponding to the new user, new item, and both new problems. In the training stage, a forest is built for the aggregated training set, where each leaf is assigned a distribution of discrete rating. In the testing stage, we present four predicting approaches to compute evaluation values based on the distribution of each tree. Experiments results on the well-known MovieLens dataset show that the aggregated approach maintains an acceptable level of accuracy. |
| format | Article |
| id | doaj-art-ac287aad99c744faaa517a93c35e15ff |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ac287aad99c744faaa517a93c35e15ff2025-08-20T03:39:14ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/649596649596Aggregated Recommendation through Random ForestsHeng-Ru Zhang0Fan Min1Xu He2School of Computer Science, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Computer Science, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Computer Science, Southwest Petroleum University, Chengdu 610500, ChinaAggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation. In this paper, we propose a random forest approach to create aggregated recommender systems. The approach is used to predict the rating of a group of users to a kind of items. In the preprocessing stage, we merge user, item, and rating information to construct an aggregated decision table, where rating information serves as the decision attribute. We also model the data conversion process corresponding to the new user, new item, and both new problems. In the training stage, a forest is built for the aggregated training set, where each leaf is assigned a distribution of discrete rating. In the testing stage, we present four predicting approaches to compute evaluation values based on the distribution of each tree. Experiments results on the well-known MovieLens dataset show that the aggregated approach maintains an acceptable level of accuracy.http://dx.doi.org/10.1155/2014/649596 |
| spellingShingle | Heng-Ru Zhang Fan Min Xu He Aggregated Recommendation through Random Forests The Scientific World Journal |
| title | Aggregated Recommendation through Random Forests |
| title_full | Aggregated Recommendation through Random Forests |
| title_fullStr | Aggregated Recommendation through Random Forests |
| title_full_unstemmed | Aggregated Recommendation through Random Forests |
| title_short | Aggregated Recommendation through Random Forests |
| title_sort | aggregated recommendation through random forests |
| url | http://dx.doi.org/10.1155/2014/649596 |
| work_keys_str_mv | AT hengruzhang aggregatedrecommendationthroughrandomforests AT fanmin aggregatedrecommendationthroughrandomforests AT xuhe aggregatedrecommendationthroughrandomforests |