Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item
  Recommendation
Yuwei Cao Liangwei Yang Chen Wang Zhiwei Liu Hao Peng Chenyu You Philip S. Yu
Recommendation systems suffer in the strict cold-start (SCS) scenario, where
the user-item interactions are entirely unavailable. The ID-based approaches
completely fail to work. Cold-start recommenders, on the other hand, leverage
item contents to map the new items to the existing ones. However, the existing
SCS recommenders explore item contents in coarse-grained manners that introduce
noise or information loss. Moreover, informative data sources other than item
contents, such as users' purchase sequences and review texts, are ignored. We
explore the role of the fine-grained item attributes in bridging the gaps
between the existing and the SCS items and pre-train a knowledgeable
item-attribute graph for SCS item recommendation. Our proposed framework,
ColdGPT, models item-attribute correlations into an item-attribute graph by
extracting fine-grained attributes from item contents. ColdGPT then transfers
knowledge into the item-attribute graph from various available data sources,
i.e., item contents, historical purchase sequences, and review texts of the
existing items, via multi-task learning. To facilitate the positive transfer,
ColdGPT designs submodules according to the natural forms of the data sources
and coordinates the multiple pre-training tasks via unified
alignment-and-uniformity losses. Our pre-trained item-attribute graph acts as
an implicit, extendable item embedding matrix, which enables the SCS item
embeddings to be easily acquired by inserting these items and propagating their
attributes' embeddings. We carefully process three public datasets, i.e., Yelp,
Amazon-home, and Amazon-sports, to guarantee the SCS setting for evaluation.
Extensive experiments show that ColdGPT consistently outperforms the existing
SCS recommenders by large margins and even surpasses models that are
pre-trained on 75-224 times more, cross-domain data on two out of four
datasets.