RecCoT: Enhancing Recommendation via Chain-of-Thought
In real-world applications, users always interact with items in multiple aspects, such as through implicit binary feedback (e.g., clicks, dislikes, long views) and explicit feedback (e.g., comments, reviews). Modern recommendation systems (RecSys) learn user-item collaborative signals from these implicit feedback signals as a large-scale binary data-streaming, subsequently recommending other highly similar items based on users' personalized historical interactions. However, from this collaborative-connection perspective, the RecSys does not focus on the actual content of the items themselves but instead prioritizes higher-probability signals of behavioral co-occurrence among items. Consequently, under this binary learning paradigm, the RecSys struggles to understand why a user likes or dislikes certain items. To alleviate it, some works attempt to utilize the content-based reviews to capture the semantic knowledge to enhance recommender models. However, most of these methods focus on predicting the ratings of reviews, but do not provide a human-understandable explanation.
Code (1)
Tasks
Recommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Improving LLM-powered Recommendations with Personalized Information
Due to the lack of explicit reasoning modeling, existing LLM-powered recommendations fail to leverage LLMs' reasoning capabilities effectively. In this paper, we propose a pipeline called CoT-Rec, which integrates two ke…
Recommendation SystemsContrastive Chain-of-Thought Prompting
Despite the success of chain of thought in enhancing language model reasoning, the underlying process remains less well understood. Although logically sound reasoning appears inherently crucial for chain of thought, prio…
Language ModelingLanguage ModellingvalidUnleashing the Power of Large Language Model for Denoising Recommendation
Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve incorporating auxiliary information or …
DenoisingLanguage ModelingLanguage ModellingLarge Language Model+2SleepCoT: A Lightweight Personalized Sleep Health Model via Chain-of-Thought Distillation
We present a novel approach to personalized sleep health management using few-shot Chain-of-Thought (CoT) distillation, enabling small-scale language models (> 2B parameters) to rival the performance of large language mo…
ManagementLogiCoT: Logical Chain-of-Thought Instruction-Tuning
Generative Pre-trained Transformer 4 (GPT-4) demonstrates impressive chain-of-thought reasoning ability. Recent work on self-instruction tuning, such as Alpaca, has focused on enhancing the general proficiency of models.…
Logical ReasoningText Generation