Proxy-based Item Representation for Attribute and Context-aware Recommendation
CoRR(2023)
摘要
Neural network approaches in recommender systems have shown remarkable
success by representing a large set of items as a learnable vector embedding
table. However, infrequent items may suffer from inadequate training
opportunities, making it difficult to learn meaningful representations. We
examine that in attribute and context-aware settings, the poorly learned
embeddings of infrequent items impair the recommendation accuracy. To address
such an issue, we propose a proxy-based item representation that allows each
item to be expressed as a weighted sum of learnable proxy embeddings. Here, the
proxy weight is determined by the attributes and context of each item and may
incorporate bias terms in case of frequent items to further reflect
collaborative signals. The proxy-based method calculates the item
representations compositionally, ensuring each representation resides inside a
well-trained simplex and, thus, acquires guaranteed quality. Additionally, that
the proxy embeddings are shared across all items allows the infrequent items to
borrow training signals of frequent items in a unified model structure and
end-to-end manner. Our proposed method is a plug-and-play model that can
replace the item encoding layer of any neural network-based recommendation
model, while consistently improving the recommendation performance with much
smaller parameter usage. Experiments conducted on real-world recommendation
benchmark datasets demonstrate that our proposed model outperforms
state-of-the-art models in terms of recommendation accuracy by up to 17% while
using only 10% of the parameters.
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