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.

Neural Collaborative Filtering

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our implemented, open-source (https://github.com/hexiangnan/sigir16-eals) eALS consistently outperforms state-of-the-art implicit MF methods.

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the basic idea to predict a personalized ranking over a set of items for an individual user with the similarities among users and items. In this paper, we propose a novel matrix factorization model with neural network architecture. Firstly, we construct a user-item matrix with explicit ratings and non-preference implicit feedback. With this matrix as the input, we present a deep structure learning architecture to learn a common low dimensional space for the representations of users and items. Secondly, we design a new loss function based on binary cross entropy, in which we consider both explicit ratings and implicit feedback for a better optimization. The experimental results show the effectiveness of both our proposed model and the loss function. On several benchmark datasets, our model outperformed other stateof-the-art methods. We also conduct extensive experiments to evaluate the performance within different experimental settings.

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Deep matrix factorization models for recommender systems

Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations. In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy. We first extract explicit product features (i.e. aspects) and user opinions by phrase-level sentiment analysis on user reviews, then generate both recommendations and disrecommendations according to the specific product features to the user's interests and the hidden features learned. Besides, intuitional feature-level explanations about why an item is or is not recommended are generated from the model. Offline experimental results on several real-world datasets demonstrate the advantages of our framework over competitive baseline algorithms on both rating prediction and top-K recommendation tasks. Online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior.

Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

Reviews information is dominant for users to make online purchasing decisions in e-commerces. However, the usefulness of reviews is varied. We argue that less-useful reviews hurt model's performance, and are also less meaningful for user's reference. While some existing models utilize reviews for improving the performance of recommender systems, few of them consider the usefulness of reviews for recommendation quality. In this paper, we introduce a novel attention mechanism to explore the usefulness of reviews, and propose a Neural Attentional Regression model with Review-level Explanations (NARRE) for recommendation. Specifically, NARRE can not only predict precise ratings, but also learn the usefulness of each review simultaneously. Therefore, the highly-useful reviews are obtained which provide review-level explanations to help users make better and faster decisions. Extensive experiments on benchmark datasets of Amazon and Yelp on different domains show that the proposed NARRE model consistently outperforms the state-of-the-art recommendation approaches, including PMF, NMF, SVD++, HFT, and DeepCoNN in terms of rating prediction, by the proposed attention model that takes review usefulness into consideration. Furthermore, the selected reviews are shown to be effective when taking existing review-usefulness ratings in the system as ground truth. Besides, crowd-sourcing based evaluations reveal that in most cases, NARRE achieves equal or even better performances than system's usefulness rating method in selecting reviews. And it is flexible to offer great help on the dominant cases in real e-commerce scenarios when the ratings on review-usefulness are not available in the system.

Neural Attentional Rating Regression with Review-level Explanations

Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by extracting from user ratings. Aside from users' ratings, their affiliated reviews often provide the rationale for their ratings and identify what aspects of the item they cared most about. We explore the rich evidence source of aspects in user reviews to improve top-N recommendation. By extracting aspects (i.e., the specific properties of items) from textual reviews, we enrich the user--item binary relation to a user--item--aspect ternary relation. We model the ternary relation as a heterogeneous tripartite graph, casting the recommendation task as one of vertex ranking. We devise a generic algorithm for ranking on tripartite graphs -- TriRank -- and specialize it for personalized recommendation. Experiments on two public review datasets show that it consistently outperforms state-of-the-art methods. Most importantly, TriRank endows the recommender system with a higher degree of explainability and transparency by modeling aspects in reviews. It allows users to interact with the system through their aspect preferences, assisting users in making informed decisions.

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TriRank: Review-aware Explainable Recommendation by Modeling Aspects

Clustering Web 2.0 items (i.e., web resources like videos, images) into semantic groups benefits many applications, such as organizing items, generating meaningful tags and improving web search. In this paper, we systematically investigate how user-generated comments can be used to improve the clustering of Web 2.0 items. In our preliminary study of Last.fm, we find that the two data sources extracted from user comments -- the textual comments and the commenting users -- provide complementary evidence to the items' intrinsic features. These sources have varying levels of quality, but we importantly we find that incorporating all three sources improves clustering. To accommodate such quality imbalance, we invoke multi-view clustering, in which each data source represents a view, aiming to best leverage the utility of different views. To combine multiple views under a principled framework, we propose CoNMF (Co-regularized Non-negative Matrix Factorization), which extends NMF for multi-view clustering by jointly factorizing the multiple matrices through co-regularization. Under our CoNMF framework, we devise two paradigms -- pair-wise CoNMF and cluster-wise CoNMF -- and propose iterative algorithms for their joint factorization. Experimental results on Last.fm and Yelp datasets demonstrate the effectiveness of our solution. In Last.fm, CoNMF betters k-means with a statistically significant F1 increase of 14%, while achieving comparable performance with the state-of-the-art multi-view clustering method CoSC (Co-regularized Spectral Clustering). On a Yelp dataset, CoNMF outperforms the best baseline CoSC with a statistically significant performance gain of 7%.

/pdf/comment-based-multi-view-clustering-of-web-2-0-items-xg71qocmle.pdf

Comment-based multi-view clustering of web 2.0 items

We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and truthful incentive mechanism to encourage users to participate. To address the challenge, we propose a novel truthful online auction mechanism that can efficiently learn to make irreversible online decisions on winner selections for new MCS systems without requiring previous knowledge of users. Moreover, we theoretically prove that our incentive possesses truthfulness, individual rationality and computational efficiency. Extensive simulation results under both real and synthetic traces demonstrate that our incentive mechanism can reduce the payment of the platform, increase the utility of the platform and social welfare.

/pdf/a-truthful-incentive-mechanism-for-online-recruitment-in-r31o6p8yld.pdf

A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System.

A truthful double auction for two-sided heterogeneous mobile crowdsensing markets

Cooperative content offloading is a promising technology to lessen heavy burden of wireless networks and improve the quality of downloading services. Since few users are voluntary in providing free assistance, auction-based incentive mechanisms are designed to encourage participation. In existing auction-based incentive mechanisms, each provider only acts as a service seller. However, a provider could also be a partner of the requestor if having interest in the requested content. This dual identity of the provider can improve the quality of its service and cut down the payment of requestor. Based on this observation, we propose an auction-based incentive mechanism named CADRE. To the best of our knowledge, CADRE is the first auction-based incentive mechanism that considers the provider's dual identity in cooperative content offloading applications. We prove that CADRE possesses attractive characteristics, i.e., truthfulness, lightweight and privacy protection. Besides, we also demonstrate that CADRE outperforms the traditional multi-attribute second-score sealed reverse auction. Our simulation results verify the theoretical analysis.

Xiao Chen

Papers

TriRank: Review-aware Explainable Recommendation by Modeling Aspects

Comment-based multi-view clustering of web 2.0 items

A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System.

A truthful double auction for two-sided heterogeneous mobile crowdsensing markets

Partner-recruitment: Incentive mechanism for content offloading