Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape

Regularising Factorised Models for Venue Recommendation using Friends and their Comments

Venue recommendation is an important capability of Location-Based Social Networks such as Yelp and Foursquare. Matrix Factorisation (MF) is a collaborative filtering-based approach that can effectively recommend venues that are relevant to the users' preferences, by training upon either implicit or explicit feedbacks (e.g. check-ins or venue ratings) that these users express about venues. However, MF suffers in that users may only have rated very few venues. To alleviate this problem, recent literature have leveraged additional sources of evidence, e.g. using users' social friendships to reduce the complexity of - or regularise - the MF model, or identifying similar venues based on their comments. This paper argues for a combined regularisation model, where the venues suggested for a user are influenced by friends with similar tastes (as defined by their comments). We propose a MF regularisation technique that seamlessly incorporates both social network information and textual comments, by exploiting word embeddings to estimate a semantic similarity of friends based on their explicit textual feedback, to regularise the complexity of the factorised model. Experiments on a large existing dataset demonstrate that our proposed regularisation model is promising, and can enhance the prediction accuracy of several state-of-the-art matrix factorisation-based approaches

LRMM: Learning to Recommend with Missing Modalities

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201