Consistency-Aware Recommendation for User-Generated ItemList Continuation
Yun He, Yin Zhang, Weiwen Liu, James Caverlee

TL;DR
This paper introduces a novel consistency-aware recommendation model for user-generated item lists that dynamically adapts to evolving user preferences across diverse content types, outperforming existing methods.
Contribution
It proposes a generalizable approach combining two preference models via a novel gating network to improve list continuation accuracy.
Findings
Effective across multiple content types (songs, books, answers)
Outperforms state-of-the-art recommendation methods
Demonstrates adaptability to evolving user preferences
Abstract
User-generated item lists are popular on many platforms. Examples include video-based playlists on YouTube, image-based lists (or"boards") on Pinterest, book-based lists on Goodreads, and answer-based lists on question-answer forums like Zhihu. As users create these lists, a common challenge is in identifying what items to curate next. Some lists are organized around particular genres or topics, while others are seemingly incoherent, reflecting individual preferences for what items belong together. Furthermore, this heterogeneity in item consistency may vary from platform to platform, and from sub-community to sub-community. Hence, this paper proposes a generalizable approach for user-generated item list continuation. Complementary to methods that exploit specific content patterns (e.g., as in song-based playlists that rely on audio features), the proposed approach models the…
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Taxonomy
TopicsRecommender Systems and Techniques · Topic Modeling · Multimodal Machine Learning Applications
