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Ruining He

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4 papers

1 author row

IJCAI Conference 2018 Conference Paper

Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior

Ruining He
Wang-Cheng Kang
Julian McAuley

Modeling the complex interactions between users and items is at the core of designing successful recommender systems. One key task consists of predicting users’ personalized sequential behavior, where the challenge mainly lies in modeling ‘third-order’ interactions between a user, her previously visited item(s), and the next item to consume. In this paper, we propose a unified method, TransRec, to model such interactions for largescale sequential prediction. Methodologically, we embed items into a ‘transition space’ where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets.

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IJCAI Conference 2017 Conference Paper

SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation

Chenwei Cai
Ruining He
Julian McAuley

Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in isolation, but by leveraging information from related events; such information could include signals from social relationships or from the sequence of recent activities. Both types of additional information can be used to improve the performance of state-of-the-art matrix factorization-based techniques. In this paper, we propose new methods to combine both social and sequential information simultaneously, in order to further improve recommendation performance. We show these techniques to be particularly effective when dealing with sparsity and cold-start issues in several large, real-world datasets.

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IJCAI Conference 2016 Conference Paper

Sherlock: Sparse Hierarchical Embeddings for Visually-Aware One-Class Collaborative Filtering

Ruining He
Chunbin Lin
Jianguo Wang
Julian McAuley

Building successful recommender systems requires uncovering the underlying dimensions that describe the properties of items as well as users' preferences toward them. In domains like clothing recommendation, explaining users' preferences requires modeling the visual appearance of the items in question. This makes recommendation especially challenging, due to both the complexity and subtlety of people's visual preferences, as well as the scale and dimensionality of the data and features involved. Ultimately, a successful model should be capable of capturing considerable variance across different categories and styles, while still modeling the commonalities explained by global structures in order to combat the sparsity (e. g. cold-start), variability, and scale of real-world datasets. Here, we address these challenges by building such structures to model the visual dimensions across different product categories. With a novel hierarchical embedding architecture, our method accounts for both high-level (colorfulness, darkness, etc. ) and subtle (e. g. casualness) visual characteristics simultaneously.

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AAAI Conference 2016 Conference Paper

VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback

Ruining He
Julian McAuley

Modern recommender systems model people and items by discovering or ‘teasing apart’ the underlying dimensions that encode the properties of items and users’ preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people’s opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people’s feedback. This not only leads to signiﬁcantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that inﬂuence people’s opinions.

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