Incorporating couplings into collaborative filtering

University of Technology Sydney. Faculty of Engineering and Information Technology.Recommender Systems (RS) have been proposed to help users tackle information overload by suggesting potentially interesting items to users. A typical RS usually has a set of users and items with various rating preferences. The key task of RS is to predict an unknown rating or to recommend relevant items to a given user. Many existing recommendation methods such as Collaborative Filtering (CF), Content-based Recommendation, and Hybrid Filtering often assume that users, items and their attributes are identically and independently distributed. In the real world, however, these objects and their attributes are often coupled with each other through explicit or implicit relations. On one hand, users are often connected through social or trust relations, and items are interacted with linkage or citation relations. On the other hand, the attributes of users or items are also more or less coupled with each other. These dependent relations clearly demonstrate that the users, items, and their attributes in RS are not identically and independently distributed (non-IID), which is rarely considered in most existing recommendation methods. The non-IID RS have emerged with the consideration of non-IID characteristics into RS. A main challenge in non-IID RS is to analyse and model the coupling relations between users and between items. In this dissertation, we aim to improve recommendation effectiveness by incorporating the coupling relations into RS. The main contributions of the dissertation are summarized as follows: (1) We propose three novel neighbourhood-based CF methods including coupled user-based CF, coupled item-based CF, and coupled CF. Specifically, we first apply a novel coupled object similarity to compute the coupling relations between users and between items based on their attributes. We then integrate the user and item couplings into the neighbourhood-based CF to produce the proposed methods by inventing new similarity measures. (2) We propose three novel model-based CF methods including coupled user-based matrix factorization (CUMF), coupled item-based matrix factorization (CIMF), and coupled matrix factorization (CMF). CUMF and CIMF respectively integrate the attribute-based user couplings and item couplings into MF, and CMF incorporates the user couplings, item couplings, and the user-item rating matrix together into MF. (3) We propose a two-level matrix factorization recommendation model which integrates the textual semantic couplings between items and the user-item rating matrix together. (4) We conduct experiments to evaluate the effectiveness of incorporating the couplings into non-IID RS

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Joint Deep Modeling of Users and Items Using Reviews for Recommendation

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.Comment: WSDM 201

Learning from Multi-View Multi-Way Data via Structural Factorization Machines

Real-world relations among entities can often be observed and determined by different perspectives/views. For example, the decision made by a user on whether to adopt an item relies on multiple aspects such as the contextual information of the decision, the item's attributes, the user's profile and the reviews given by other users. Different views may exhibit multi-way interactions among entities and provide complementary information. In this paper, we introduce a multi-tensor-based approach that can preserve the underlying structure of multi-view data in a generic predictive model. Specifically, we propose structural factorization machines (SFMs) that learn the common latent spaces shared by multi-view tensors and automatically adjust the importance of each view in the predictive model. Furthermore, the complexity of SFMs is linear in the number of parameters, which make SFMs suitable to large-scale problems. Extensive experiments on real-world datasets demonstrate that the proposed SFMs outperform several state-of-the-art methods in terms of prediction accuracy and computational cost.Comment: 10 page