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Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
Ruining He, Julian McAuley
Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, or place t…
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Ruining He, Chen Fang, Zhaowen Wang +1
Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand on…
Sherlock: Sparse Hierarchical Embeddings for Visually-aware One-class Collaborative Filtering
Ruining He, Chunbin Lin, Jianguo Wang +1
Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains li…
Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items
Ruining He, Chunbin Lin, Julian McAuley
To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alter…
Learning Compatibility Across Categories for Heterogeneous Item Recommendation
Ruining He, Charles Packer, Julian McAuley
Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compati…
Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering
Ruining He, Julian McAuley
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeli…