Summary of Exploiting Preferences in Loss Functions For Sequential Recommendation Via Weak Transitivity, by Hyunsoo Chung et al.
Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity
by Hyunsoo Chung, Jungtaek Kim, Hyungeun Jo, Hyungwon Choi
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recommender system’s design is heavily influenced by its optimization objective, which affects how user intent is modeled from previous interactions. The paper focuses on existing loss functions, categorized into pairwise, pointwise, and setwise approaches. While these methods are effective, they often assign binary labels to next observed items as positive and remaining items as negative. This approach prioritizes the positive item’s recommendation score while neglecting potential structures induced by varying preferences between other unobserved items. To address this limitation, the authors propose a novel method that extends original objectives to explicitly model different levels of preferences as relative orders between their scores. The proposed method outperforms baseline objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A recommender system is a way for a computer program to suggest things you might like based on what you’ve done before. To make this work, the program needs an “optimizer” that decides how well it’s doing. Right now, most optimizers use one of three main methods: comparing pairs of items, looking at each item individually, or grouping similar items together. The problem is that these methods often treat everything except what you liked before as equally bad, which isn’t very realistic because people like different things in different ways. To fix this, the authors came up with a new way to look at preferences that’s more detailed and accurate. |
Keywords
* Artificial intelligence * Optimization
