About: Exploiting heterogeneous information networks (HIN) to top-N recommendation has been shown to alleviate the data sparsity problem present in recommendation systems. This requires careful effort in extracting relevant knowledge from HIN. However, existing models in this setting have the following shortcomings. Mainly, they are not end-to-end, which puts the burden on the system to first learn similarity or commuting matrix offline using some manually selected meta-paths before we train for the top-N recommendation objective. Further, they do not attentively extract user-specific information from HIN, which is essential for personalization. To address these challenges, we propose an end-to-end neural network model – GAMMA (Graph and Multi-view Memory Attention mechanism). We aim to replace the offline meta-path based similarity or commuting matrix computation with a graph attention mechanism. Besides, with different semantics of items in HIN, we propose a multi-view memory attention mechanism to learn more profound user-specific item views. Experiments on three real-world datasets demonstrate the effectiveness of our model for top-N recommendation setting.

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About: Exploiting heterogeneous information networks (HIN) to top-N recommendation has been shown to alleviate the data sparsity problem present in recommendation systems. This requires careful effort in extracting relevant knowledge from HIN. However, existing models in this setting have the following shortcomings. Mainly, they are not end-to-end, which puts the burden on the system to first learn similarity or commuting matrix offline using some manually selected meta-paths before we train for the top-N recommendation objective. Further, they do not attentively extract user-specific information from HIN, which is essential for personalization. To address these challenges, we propose an end-to-end neural network model – GAMMA (Graph and Multi-view Memory Attention mechanism). We aim to replace the offline meta-path based similarity or commuting matrix computation with a graph attention mechanism. Besides, with different semantics of items in HIN, we propose a multi-view memory attention mechanism to learn more profound user-specific item views. Experiments on three real-world datasets demonstrate the effectiveness of our model for top-N recommendation setting. Goto Sponge NotDistinct Permalink

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type	abstract
value	Exploiting heterogeneous information networks (HIN) to top-N recommendation has been shown to alleviate the data sparsity problem present in recommendation systems. This requires careful effort in extracting relevant knowledge from HIN. However, existing models in this setting have the following shortcomings. Mainly, they are not end-to-end, which puts the burden on the system to first learn similarity or commuting matrix offline using some manually selected meta-paths before we train for the top-N recommendation objective. Further, they do not attentively extract user-specific information from HIN, which is essential for personalization. To address these challenges, we propose an end-to-end neural network model – GAMMA (Graph and Multi-view Memory Attention mechanism). We aim to replace the offline meta-path based similarity or commuting matrix computation with a graph attention mechanism. Besides, with different semantics of items in HIN, we propose a multi-view memory attention mechanism to learn more profound user-specific item views. Experiments on three real-world datasets demonstrate the effectiveness of our model for top-N recommendation setting.
subject	Graph theory Classification algorithms Concepts in logic World Wide Web
part of	GAMMA: A Graph and Multi-view Memory Attention Mechanism for Top-N Heterogeneous Recommendation
is abstract of	GAMMA: A Graph and Multi-view Memory Attention Mechanism for Top-N Heterogeneous Recommendation
is hasSource of	covid:ann/target/935bb97a7c1b917f4798b6895e7ddf8b2fb9f85c covid:ann/target/f0c167bda3b3f214170dae9c0f3dd008418536f9 covid:ann/target/94417ba154908bbffb1e8a0ddcdac088cd34aa38 covid:ann/target/b5dc94619092c1e6ed670bdf5dbd66bafd99b627 covid:ann/target/bf80b4bb76cd8e2983ace818ae8fda70d7950a14 »more»

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