Modeling sequential and basket-oriented associations for top-K recommendation
Top-K recommendation is a typical task in Recommender Systems. In traditional approaches, it mainly relies on the modeling of user-item associations, which emphasizes the user-specific factor or personalization. Here, we investigate another direction that models item-item associations, especially wi...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2019
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| Online Access: | https://ink.library.smu.edu.sg/etd_coll/198 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1198&context=etd_coll |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Top-K recommendation is a typical task in Recommender Systems. In traditional approaches, it mainly relies on the modeling of user-item associations, which emphasizes the user-specific factor or personalization. Here, we investigate another direction that models item-item associations, especially with the notions of sequence-aware and basket-level adoptions . Sequences are created by sorting item adoptions chronologically. The associations between items along sequences, referred to as “sequential associations”, indicate the influence of the preceding adoptions on the following adoptions. Considering a basket of items consumed at the same time step (e.g., a session, a day), “basket-oriented associations” imply correlative dependencies among these items. In this dissertation, we present research works on modeling “sequential & basket-oriented associations” independently and jointly for the Top-K recommendation task. |
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