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

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

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

Clustering-Based Personalization

Recommendation systems have been the most emerging technology in the last decade as one of the key parts in e-commerce ecosystem. Businesses offer a wide variety of items and contents through different channels such as Internet, Smart TVs, Digital Screens, etc. The number of these items sometimes goes over millions for some businesses. Therefore, users can have trouble finding the products that they are looking for. Recommendation systems address this problem by providing powerful methods which enable users to filter through large information and product space based on their preferences. Moreover, users have different preferences. Thus, businesses can employ recommendation systems to target more audiences by addressing them with personalized content. Recent studies show a significant improvement of revenue and conversion rate for recommendation system adopters. Accuracy, scalability, comprehensibility, and data sparsity are main challenges in recommendation systems. Businesses need practical and scalable recommendation models which accurately personalize millions of items for millions of users in real-time. They also prefer comprehensible recommendations to understand how these models target their users. However, data sparsity and lack of enough data about items, users and their interests prevent personalization models to generate accurate recommendations. In Chapter 1, we first describe basic definitions in recommendation systems. We then shortly review our contributions and their importance in this thesis. Then in Chapter 2, we review the major solutions in this context. Traditional recommendation system methods usually make a rating matrix based on the observed ratings of users on items. This rating matrix is then employed in different data mining techniques to predict the unknown rating values based on the known values. In a novel solution, in Chapter 3, we capture the mean interest of the cluster of users on the cluster of items in a cluster-level rating matrix. We first cluster users and items separately based on the known ratings. In a new matrix, we then present the interest of each user clusters on each item clusters by averaging the ratings of users inside each user cluster on the items belonging to each item cluster. Then, we apply the matrix factorization method on this coarse matrix to predict the future cluster-level interests. Our final rating prediction includes an aggregation of the traditional user-item rating predictions and our cluster-level rating predictions. Generating personalized recommendation for cold-start users, or users with only few feedback, is a big challenge in recommendation systems. Employing any available information from these users in other domains is crucial to improve their recommendation accuracy. Thus, in Chapter 4, we extend our proposed clustering-based recommendation model by including the auxiliary feedback in other domains. In a new cluster-level rating matrix, we capture the cluster-level interests between the domains to reduce the sparsity of the known ratings. By factorizing this cross-domain rating matrix, we effectively utilize data from auxiliary domains to achieve better recommendations in the target domain, especially for cold-start users. In Chapter 5, we apply our proposed clustering-based recommendation system to Morphio platform used in a local digital marketing agency called Arcane inc. Morphio is an smart adaptive web platform, which is designed to help Arcane to produce smart contents and target more audiences. In Morphio, agencies can define multiple versions of content including texts, images, colors, and so on for their web pages. A personalization module then matches a version of content to each user using their profiles. Our ongoing real time experiment shows a significant improvement of user conversion employing our proposed clustering-based personalization. Finally, in Chapter 6, we present a summary and conclusions for this thesis. Parts of this thesis were submitted or published in peer-review journal and conferences including ACM Transactions on Knowledge Discovery from Data and ACM Conferences on Recommender Systems

CoNet: Collaborative Cross Networks for Cross-Domain Recommendation

The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfer learning is a class of algorithms underlying these techniques. In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model. In contrast to the matrix factorization based cross-domain techniques, our method is deep transfer learning, which can learn complex user-item interaction relationships. We assume that hidden layers in two base networks are connected by cross mappings, leading to the collaborative cross networks (CoNet). CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa. CoNet is achieved in multi-layer feedforward networks by adding dual connections and joint loss functions, which can be trained efficiently by back-propagation. The proposed model is thoroughly evaluated on two large real-world datasets. It outperforms baselines by relative improvements of 7.84\% in NDCG. We demonstrate the necessity of adaptively selecting representations to transfer. Our model can reduce tens of thousands training examples comparing with non-transfer methods and still has the competitive performance with them.Comment: Deep transfer learning for recommender system

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure