Neural Collaborative Reasoning

• URL: http://arxiv.org/abs/2005.08129v5
• Date: Mon, 3 May 2021 02:06:05 GMT
• Title: Neural Collaborative Reasoning
• Authors: Hanxiong Chen, Shaoyun Shi, Yunqi Li, Yongfeng Zhang
• Abstract summary: We propose Collaborative Filtering (CF) to Collaborative Reasoning (CR) CR means that each user knows part of the reasoning space, and they collaborate for reasoning in the space to estimate preferences for each other. We integrate the power of representation learning and logical reasoning, where representations capture similarity patterns in data from perceptual perspectives.
• Score: 31.03627817834551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i.e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions. However, as a cognition rather than a perception intelligent task, recommendation requires not only the ability of pattern recognition and matching from data, but also the ability of cognitive reasoning in data. In this paper, we propose to advance Collaborative Filtering (CF) to Collaborative Reasoning (CR), which means that each user knows part of the reasoning space, and they collaborate for reasoning in the space to estimate preferences for each other. Technically, we propose a Neural Collaborative Reasoning (NCR) framework to bridge learning and reasoning. Specifically, we integrate the power of representation learning and logical reasoning, where representations capture similarity patterns in data from perceptual perspectives, and logic facilitates cognitive reasoning for informed decision making. An important challenge, however, is to bridge differentiable neural networks and symbolic reasoning in a shared architecture for optimization and inference. To solve the problem, we propose a modularized reasoning architecture, which learns logical operations such as AND ($\wedge$), OR ($\vee$) and NOT ($\neg$) as neural modules for implication reasoning ($\rightarrow$). In this way, logical expressions can be equivalently organized as neural networks, so that logical reasoning and prediction can be conducted in a continuous space. Experiments on real-world datasets verified the advantages of our framework compared with both shallow, deep and reasoning models.

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