CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect
Transfer Network
- URL: http://arxiv.org/abs/2005.10549v2
- Date: Sat, 23 May 2020 07:13:11 GMT
- Title: CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect
Transfer Network
- Authors: Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun
- Abstract summary: We propose a cross-domain recommendation framework via aspect transfer network for cold-start users (named CATN)
CATN is devised to extract multiple aspects for each user and each item from their review documents, and learn aspect correlations across domains with an attention mechanism.
On real-world datasets, the proposed CATN outperforms SOTA models significantly in terms of rating prediction accuracy.
- Score: 49.35977893592626
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In a large recommender system, the products (or items) could be in many
different categories or domains. Given two relevant domains (e.g., Book and
Movie), users may have interactions with items in one domain but not in the
other domain. To the latter, these users are considered as cold-start users.
How to effectively transfer users' preferences based on their interactions from
one domain to the other relevant domain, is the key issue in cross-domain
recommendation. Inspired by the advances made in review-based recommendation,
we propose to model user preference transfer at aspect-level derived from
reviews. To this end, we propose a cross-domain recommendation framework via
aspect transfer network for cold-start users (named CATN). CATN is devised to
extract multiple aspects for each user and each item from their review
documents, and learn aspect correlations across domains with an attention
mechanism. In addition, we further exploit auxiliary reviews from like-minded
users to enhance a user's aspect representations. Then, an end-to-end
optimization framework is utilized to strengthen the robustness of our model.
On real-world datasets, the proposed CATN outperforms SOTA models significantly
in terms of rating prediction accuracy. Further analysis shows that our model
is able to reveal user aspect connections across domains at a fine level of
granularity, making the recommendation explainable.
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