2 research outputs found
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Graph collaborative filtering, which learns user and item representations
through message propagation over the user-item interaction graph, has been
shown to effectively enhance recommendation performance. However, most current
graph collaborative filtering models mainly construct the interaction graph on
a single behavior domain (e.g. click), even though users exhibit various types
of behaviors on real-world platforms, including actions like click, cart, and
purchase. Furthermore, due to variations in user engagement, there exists an
imbalance in the scale of different types of behaviors. For instance, users may
click and view multiple items but only make selective purchases from a small
subset of them. How to alleviate the behavior imbalance problem and utilize
information from the multiple behavior graphs concurrently to improve the
target behavior conversion (e.g. purchase) remains underexplored. To this end,
we propose IMGCF, a simple but effective model to alleviate behavior data
imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF
utilizes a multi-task learning framework for collaborative filtering on
multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF
improves representation learning on the sparse behavior by leveraging
representations learned from the behavior domain with abundant data volumes.
Experiments on two widely-used multi-behavior datasets demonstrate the
effectiveness of IMGCF.Comment: accepted by ICDM2023 Worksho
Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation
Online local-life service platforms provide services like nearby daily
essentials and food delivery for hundreds of millions of users. Different from
other types of recommender systems, local-life service recommendation has the
following characteristics: (1) spatiotemporal periodicity, which means a user's
preferences for items vary from different locations at different times. (2)
spatiotemporal collaborative signal, which indicates similar users have similar
preferences at specific locations and times. However, most existing methods
either focus on merely the spatiotemporal contexts in sequences, or model the
user-item interactions without spatiotemporal contexts in graphs. To address
this issue, we design a new method named SPCS in this paper. Specifically, we
propose a novel spatiotemporal graph transformer (SGT) layer, which explicitly
encodes relative spatiotemporal contexts, and aggregates the information from
multi-hop neighbors to unify spatiotemporal periodicity and collaborative
signal. With extensive experiments on both public and industrial datasets, this
paper validates the state-of-the-art performance of SPCS.Comment: KDAH CIKM'23 Worksho