Zhiping Gu

Modeling Semantic Web Service using Semantic Templates

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.

Diversifying Personalized Recommendation with User-session Context

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.

Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains

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.

Improving the Quality of Recommendations for Users and Items in the Tail of Distribution

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.

Bayesian Heteroskedastic Choice Modeling on Non-identically Distributed Linkages

Choice modeling (CM) aims to describe and predict choices according to attributes of subjects and options. If we presume each choice making as the formation of link between subjects and options, immediately CM can be bridged to link analysis and prediction (LAP) problem. However, such a mapping is often not trivial and straightforward. In LAP problems, the only available observations are links among objects but their attributes are often inaccessible. Therefore, we extend CM into a latent feature space to avoid the need of explicit attributes. Moreover, LAP is usually based on binary linkage assumption that models observed links as positive instances and unobserved links as negative instances. Instead, we use a weaker assumption that treats unobserved links as pseudo negative instances. Furthermore, most subjects or options may be quite heterogeneous due to the long-tail distribution, which is failed to capture by conventional LAP approaches. To address above challenges, we propose a Bayesian heteroskedastic choice model to represent the non-identically distributed linkages in the LAP problems. Finally, the empirical evaluation on real-world datasets proves the superiority of our approach.

Service Discovery and Recommendation in Rough Hierarchical Granular Computing

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.