| Autor: |
Kathryn Hempstalk, Thomas Kernreiter, Seamus Jolly, Hang Yu, Lester Litchfield |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). |
| DOI: |
10.1109/hpbdis.2019.8735479 |
| Popis: |
Driven by the rapid development of global e-commerce, recommender systems have become a hot topic across both industry and academia because they offer a potential source of business revenue. A wide range of recommendation solutions have been developed and they can be classified as substitute and complementary: substitute recommenders offer similar items to the source item, complementary recommenders suggest items that are dissimilar to the source but are often sold as a companion item or service (such as a mobile phone (source) and case (complementary)). Both types of recommendations demonstrate unique values in specific application domains. However, the research and development of complementary recommendations still remain sparse when compared with substitute recommenders. This paper presents a brief survey on existing solutions for complementary recommendations. Our work summarizes the existing research activities and explores open questions in this field by discussing three aspects including the identification of the ground truth for complements; the recommendation models; and evaluation datasets and metrics. To the best of our knowledge, this work is one of the few surveys that provide particular insights on complementary recommendations in recent years. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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