Paper-reviewer recommendation problem in academic usually refers to recommend experts to comment on the quality of papers. How to effectively and accurately recommend reviewers for papers is a meaningful and still tough task. Generally, the representation of a paper and a reviewer is very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which slightly increases difficulty in paper-reviewer recommendation. In this paper, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC. Firstly, we introduce a HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers. Further we transform the paper-reviewer recommendation problem into a Multi-Label Classification (MLC) issue, whose multiple research labels exactly guide the learning process. It?s flexible that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Our experiments on the real dataset consists of the papers in ACM Digital Library show the effectiveness and feasibility of our method.
Recommender systems aim to capture user preferences and provide accurate recommendations to users accordingly. A collection of users may have similar preferences with each other, thus forms a community. Since such communities may not be explicitly given, they are formally defined and named Implicit Preference Communities (IPCs) in this paper. By enriching user preferences with the information of other users in the communities, the performance of recommender systems can also be enhanced. In this paper, we propose a recommendation model with Implicit Preference Communities from user ratings and social connections. To tackle the unsupervised learning limitation of IPC modeling, we propose a semi-supervised Bayesian probabilistic graphical model to capture the IPC structure for recommendation. Meanwhile following the spirit of transfer learning, both rating behaviors and social connections are introduced into the model by parameter sharing. Moreover Gibbs sampling based algorithms are proposed for parameter inferences of the models. And to meet the need for online scenarios when data arrives as a stream, a novel online sampling based parameter inference algorithm is proposed. Extensive experiments on seven real-world datasets have been conducted and compared with fourteen state-of-art recommendation algorithms. Statistically significant improvements verify the effectiveness of the proposed IPC-aware recommendation models.
[This list of reviewers has no abstract.]
Exploiting temporal effect has empirically been recognized as a promising way to improve the recommendation performance in recent years. In real-world applications, one-class data in the form of (user, item, timestamp) are usually more accessible and abundant than numerical ratings. In this paper, we focus on exploiting such one-class data in order to provide personalized next-item recommendation services. Specifically, we base our work on the framework of time-aware item-based collaborative filtering, and propose a simple yet effective bidirectional item similarity (BIS) that is able to capture sequential patterns even from noisy data. Furthermore, we extend BIS via some factorization techniques and obtain an adaptive version, i.e., adaptive BIS (ABIS), in order to better fit the behavioral data. We also design a compound weighting function that leverages the complementarity between two well-known time-aware weighting functions. With the proposed similarity measurements and weighting function, we obtain two novel collaborative filtering methods that are able to achieve significantly better performance than the state-of-the-art methods, showcasing their effectiveness for next-item recommendation.
Psychometric measures reflecting people's knowledge, ability, attitudes and personality traits are critical for many real-world applications, such as e-commerce, healthcare, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this paper, we propose a deep learning architecture, PyNDA, to extract psychometric dimensions from user-generated texts. PyNDA contains a novel representation embedding, a demographic embedding, a structural equation model (SEM) encoder, and a multi-task learning mechanism designed to work in unison to address the unique challenges associated with extracting rich, sophisticated and user-centric psychometric dimensions. Our experiments on three real-world datasets encompassing eleven psychometric dimensions, including trust, anxiety, and literacy, show that PyNDA markedly outperforms traditional feature-based classifiers as well as the state-of-the-art deep learning architectures. Ablation analysis reveals that each component of PyNDA significantly contributes to its overall performance. Collectively, the results demonstrate the efficacy of the proposed architecture for facilitating rich psychometric analysis. Our results have important implications for user-centric information extraction and retrieval systems looking to measure and incorporate psychometric dimensions.