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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | (Partial) user preference similarity as classification-based model similarity |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Semantic Web |
| Publisher | IOS Press |
| Geographical Reach | international |
| ISSN | 1570-0844 |
| Volume | 5 |
| Number | 1 |
| Page Range | 47 - 64 |
| Date | 2014 |
| Abstract Text | Recommender systems play an important role in helping people finding items they like. One type of recommender system is collaborative filtering that considers feedback of like-minded people. The fundamental assumption of collaborative filtering is that people who previously shared similar preferences behave similarly later on. This paper introduces several novel, classification-based similarity metrics that are used to compare user preferences. Furthermore, the concept of partial preference similarity based on a machine learning model is presented. For evaluation the cold-start behavior of the presented classification-based similarity metrics is evaluated in a large-scale experiment. It is shown that classification-based similarity metrics with machine learning significantly outperforms other similarity approaches in different cold-start situations under different degrees of data-sparseness. |
| Free access at | Related URL |
| Official URL | http://www.semantic-web-journal.net/content/partial-user-preference-similarity-classification-based-model-similarity |
| Digital Object Identifier | 10.3233/SW-130099 |
| Other Identification Number | merlin-id:7938 |
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