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Found 4 papers, 1 papers with code
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning
(2) It is difficult to obtain users' explicit feedback of their preference in product features.
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
A novel query-based interactive recommender system is proposed in this paper, where \textbf{personalized questions are accurately generated from millions of automatically constructed questions} in Step 1, and \textbf{the recommendation is ensured to be closely-related to users' feedback} in Step 2.
A Brand-level Ranking System with the Customized Attention-GRU Model
In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality.
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
Both content information (e. g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item.
