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

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization's environment and that they appear in different contexts. We use internet information to represent organization's environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time

Algorithms, applications and systems towards interpretable pattern mining from multi-aspect data

How do humans move around in the urban space and how do they differ when the city undergoes terrorist attacks? How do users behave in Massive Open Online courses~(MOOCs) and how do they differ if some of them achieve certificates while some of them not? What areas in the court elite players, such as Stephen Curry, LeBron James, like to make their shots in the course of the game? How can we uncover the hidden habits that govern our online purchases? Are there unspoken agendas in how different states pass legislation of certain kinds? At the heart of these seemingly unconnected puzzles is this same mystery of multi-aspect mining, i.g., how can we mine and interpret the hidden pattern from a dataset that simultaneously reveals the associations, or changes of the associations, among various aspects of the data (e.g., a shot could be described with three aspects, player, time of the game, and area in the court)? Solving this problem could open gates to a deep understanding of underlying mechanisms for many real-world phenomena. While much of the research in multi-aspect mining contribute broad scope of innovations in the mining part, interpretation of patterns from the perspective of users (or domain experts) is often overlooked. Questions like what do they require for patterns, how good are the patterns, or how to read them, have barely been addressed. Without efficient and effective ways of involving users in the process of multi-aspect mining, the results are likely to lead to something difficult for them to comprehend. This dissertation proposes the M^3 framework, which consists of multiplex pattern discovery, multifaceted pattern evaluation, and multipurpose pattern presentation, to tackle the challenges of multi-aspect pattern discovery. Based on this framework, we develop algorithms, applications, and analytic systems to enable interpretable pattern discovery from multi-aspect data. Following the concept of meaningful multiplex pattern discovery, we propose PairFac to close the gap between human information needs and naive mining optimization. We demonstrate its effectiveness in the context of impact discovery in the aftermath of urban disasters. We develop iDisc to target the crossing of multiplex pattern discovery with multifaceted pattern evaluation. iDisc meets the specific information need in understanding multi-level, contrastive behavior patterns. As an example, we use iDisc to predict student performance outcomes in Massive Open Online Courses given users' latent behaviors. FacIt is an interactive visual analytic system that sits at the intersection of all three components and enables for interpretable, fine-tunable, and scrutinizable pattern discovery from multi-aspect data. We demonstrate each work's significance and implications in its respective problem context. As a whole, this series of studies is an effort to instantiate the M^3 framework and push the field of multi-aspect mining towards a more human-centric process in real-world applications

Tensor Learning for Recovering Missing Information: Algorithms and Applications on Social Media

Real-time social systems like Facebook, Twitter, and Snapchat have been growing rapidly, producing exabytes of data in different views or aspects. Coupled with more and more GPS-enabled sharing of videos, images, blogs, and tweets that provide valuable information regarding “who”, “where”, “when” and “what”, these real-time human sensor data promise new research opportunities to uncover models of user behavior, mobility, and information sharing. These real-time dynamics in social systems usually come in multiple aspects, which are able to help better understand the social interactions of the underlying network. However, these multi-aspect datasets are often raw and incomplete owing to various unpredictable or unavoidable reasons; for instance, API limitations and data sampling policies can lead to an incomplete (and often biased) perspective on these multi-aspect datasets. This missing data could raise serious concerns such as biased estimations on structural properties of the network and properties of information cascades in social networks. In order to recover missing values or information in social systems, we identify “4S” challenges: extreme sparsity of the observed multi-aspect datasets, adoption of rich side information that is able to describe the similarities of entities, generation of robust models rather than limiting them on specific applications, and scalability of models to handle real large-scale datasets (billions of observed entries). With these challenges in mind, this dissertation aims to develop scalable and interpretable tensor-based frameworks, algorithms and methods for recovering missing information on social media. In particular, this dissertation research makes four unique contributions: _ The first research contribution of this dissertation research is to propose a scalable framework based on low-rank tensor learning in the presence of incomplete information. Concretely, we formally define the problem of recovering the spatio-temporal dynamics of online memes and tackle this problem by proposing a novel tensor-based factorization approach based on the alternative direction method of multipliers (ADMM) with the integration of the latent relationships derived from contextual information among locations, memes, and times. _ The second research contribution of this dissertation research is to evaluate the generalization of the proposed tensor learning framework and extend it to the recommendation problem. In particular, we develop a novel tensor-based approach to solve the personalized expert recommendation by integrating both the latent relationships between homogeneous entities (e.g., users and users, experts and experts) and the relationships between heterogeneous entities (e.g., users and experts, topics and experts) from the geo-spatial, topical, and social contexts. _ The third research contribution of this dissertation research is to extend the proposed tensor learning framework to the user topical profiling problem. Specifically, we propose a tensor-based contextual regularization model embedded into a matrix factorization framework, which leverages the social, textual, and behavioral contexts across users, in order to overcome identified challenges. _ The fourth research contribution of this dissertation research is to scale up the proposed tensor learning framework to be capable of handling real large-scale datasets that are too big to fit in the main memory of a single machine. Particularly, we propose a novel distributed tensor completion algorithm with the trace-based regularization of the auxiliary information based on ADMM under the proposed tensor learning framework, which is designed to scale up to real large-scale tensors (e.g., billions of entries) by efficiently computing auxiliary variables, minimizing intermediate data, and reducing the workload of updating new tensors

Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

User opinions expressed in the form of ratings can influence an individual's view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance different users place on different aspects of an item. We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.Comment: Accepted for publication in IJCAI 201