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Short-head and long-tail distributed data are widely observed in the real world. The same is true of recommender systems (RSs), where a small number of popular items dominate the choices and feedback data while the rest only account for a small amount of feedback. As a result, most RS methods tend to learn user preferences from popular items since they account for most data. However, recent research in e-commerce and marketing has shown that future businesses will obtain greater profit from long-tail selling. Yet, although the number of long-tail items and users is much larger than that of short-head items and users, in reality, the amount of data associated with long-tail items and users is much less. As a result, user preferences tend to be popularity-biased. Furthermore, insufficient data makes long-tail items and users more vulnerable to shilling attack. To improve the quality of recommendations for items and users in the tail of distribution, we propose a coupled regularization approach that consists of two latent factor models: C-HMF, for enhancing credibility, and S-HMF, for emphasizing specialty on user choices. Specifically, the estimates learned from C-HMF and S-HMF recurrently serve as the empirical priors to regularize one another. Such coupled regularization leads to the comprehensive effects of final estimates, which produce more qualitative predictions for both tail users and tail items. To assess the effectiveness of our model, we conduct empirical evaluations on large real-world datasets with various metrics. The results prove that our approach significantly outperforms the compared methods.

Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.

In the real-world environment, users have sufficient experience in their focused domains but lack experience in other domains. Recommender systems are very helpful for recommending potentially desirable items to users in unfamiliar domains, and cross-domain collaborative filtering is therefore an important emerging research topic. However, it is inevitable that the cold-start issue will be encountered in unfamiliar domains due to the lack of feedback data. The Bayesian approach shows that priors play an important role when there are insufficient data, which implies that recommendation performance can be significantly improved in cold-start domains if informative priors can be provided. Based on this idea, we propose a Weighted Irregular Tensor Factorization (WITF) model to leverage multi-domain feedback data across all users to learn the cross-domain priors w.r.t. both users and items. The features learned from WITF serve as the informative priors on the latent factors of users and items in terms of weighted matrix factorization models. Moreover, WITF is a unified framework for dealing with both explicit feedback and implicit feedback. To prove the effectiveness of our approach, we studied three typical real-world cases in which a collection of empirical evaluations were conducted on real-world datasets to compare the performance of our model and other state-of-the-art approaches. The results show the superiority of our model over comparison models.

This paper describes a new approach to normalizing microarray expression data. The novel feature is to unify the tasks of estimating normalization coefficients and identifying control gene set. Unification is realized by constructing a window function over the scatter plot defining the subset of constantly expressed genes and by affecting optimization using an iterative procedure. The structure of window function gates contributions to the control gene set used to estimate normalization coefficients. This window measures the consistency of the matched neighborhoods in the scatter plot and provides a means of rejecting control gene outliers. The recovery of normalizational regression and control gene selection are interleaved and are realized by applying coupled operations to the mean square error function. In this way, the two processes bootstrap one another. We evaluate the technique on real microarray data from breast cancer cell lines and complement the experiment with a data cluster visualization study.

Animal models remain a cornerstone of research efforts in Oncology to model the complexity of cancer progression and to discover new therapeutic approaches to disease management. With the advent of new genomic manipulation techniques such as CRISPR, and advances in mouse modeling with genetically engineered mice (GEM) and patient-derived xenografts (PDX), we can expect the development of novel and powerful animal models in the near future. These techniques are expanding our capability for pre-clinical testing of novel therapeutic agents or for n-of-1 patient-specific disease modeling. However, the reliability of animal models to faithfully recapitulate human disease, especially cancer, remains controversial, as evidenced by numerous therapeutic agents that show promise in animal models but fail in clinical trials. We propose a new scoring system called the Oncology Model Fidelity Scores, which allow researchers and clinicians to compare animal models and select those most suited for the question at hand, whether in basic science, for translational research, or within clinical applications. The formalism and tools we are developing for the analysis of mouse models and other model organisms are based on the Hallmarks of Cancer and therapeutic pathways as well as The Cancer Genome Atlas (TCGA) and other comprehensive systems. The scoring system begins with RNASEQ expression data and DNA variation analysis, and is designed for use in mapping animal models both to individual patients and to a TCGA cohort. We are applying this scoring system to mouse models available through NCI’s Oncology Model Forum and we are analyzing how these animal models compare to human cancers based on TCGA data. Ultimately, the development of an Oncology Model Fidelity Score will help to advance patient care through efficient identification and validation of animal models for a variety of applications, from pre-clinical testing of novel therapeutics to the use of patient-specific animal models. Citation Format: Debajyoti Datta, Theodore Goldstein, Zhiping Gu, Atul Butte. Oncology model fidelity scores [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-006. doi:10.1158/1538-7445.AM2017-LB-006

Service discovery is one of the most vital components involved in almost all service applications. A lot of researches have paid attention to improving the matching accuracy for the given user requirement. Without a clarified requirement specification to describe the goals user really wants to achieve, any matching algorithm is useless. Goal-oriented requirement engineering is a formal requirement analysis methodology which recursively decompose a complex requirement into a set of finer grained goals. Such a hierarchical granulation structure partitions a requirement into a family of fine grain-sized granules. In order to handle uncertainties, rough set theory is employed in granular computing. For any given imprecise user requirement, a set of ordered stratified rough set approximations can be induced over all possible partitions. These approximations are used to iteratively refine imprecise requirement and recommend goals most probably desired. A matching algorithm based on six types of granule approximation is also given to describe different matching strategies for satisfying user requirement. Through the theoretical analysis and case study, it shows that rough set theory combining with granular computing is powerful to handle imprecise requirements and also provide better service quality.

Web services have been widely used recent years. In order to enable the intelligent discovery and use by machine, the semantic information should be provided to represent various aspects of Web services. If it always need build complex semantic information from scratch to describe every aspect of Web service, it will lead to not only large redundancy and inconsistency but also low maintainability and extensibility. The customizable semantic template is proposed to resolve such issues, which enables to semantically model any aspect of Web service in a more flexible and efficient way. A universal method to automatically generate semantic template instance is also proposed to resolve the issues like high workload for building semantic information for every aspect of Web service manually and high specialized domain knowledge required.

Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.