Wenge Rong

Personalized Web Service Ranking via User Group Combining Association Rule

Web service plays an important role in implementing Service Oriented Architecture (SOA) for achieving dynamic business process. With the increased number of web services advertised in public repository, it is becoming vital to provide an efficient web service discovery and selection mechanism with respect to a user’s requirement. Considerable efforts have been made to solve this problem among which semantic based web service discovery has been attained much importance by researchers in academic and industry community. However, there is a challenge in the semantic based web service discovery process, that is, among the retrieved set of semantically equivalent web service candidates, how to discern which one is the best? In this paper, inspired by collaborative filtering idea, a web service ranking framework is proposed in which a set of users with similar interest will be firstly identified. Afterwards, association rules will be found out by analyzing all web service composition transactions related to that set of users. By combining user group and association rule mined from that group, a personalized web service ranking mechanism is achieved and the experiment shows the promising result.

Collaborative personal profiling for web service ranking and recommendation

Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer’s requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.

Autoencoder-Based Collaborative Filtering

Currently collaborative filtering is widely used in recommender systems. With the development of idea of deep learning, a lot of researches have been conducted to improve collaborative filtering by integrating deep learning techniques. In this research, we proposed an autoencoder based collaborative filtering method, in which pretraining and stacking mechanism is provided. The experimental study on commonly used MovieLens datasets have shown its potential and effectiveness in getting higher recall.

Association Rule Based Context Modeling for Web Service Discovery

Web service is essential in achieving dynamic business process. With the dramatically increased number of Web services advertised in UDDI, Web Portal or Internet, how to locate the best Web service according to a users requirement is becoming more and more important, which calls for efficient and effective Web service discovery mechanism. Considerable efforts have been attained in solving this problem among them semantic based approaches show encouraging result. However, when several semantically equivalent Web service candidates are returned by matchmaking process, how to discern which one is the most suitable one is a real challenge. This paper presents a framework by which providers context and users context has been explored to help understand the real need of a user. This framework uses association rule for context modeling of provider and user from Web service composition perspective. After modeling context, a ranking service is invoked to compare Web service candidates with users requirement and then the best suitable potential Web service will be selected.

Embedding assisted prediction architecture for event trigger identification

Molecular events normally have significant meanings since they describe important biological interactions or alternations such as binding of a protein. As a crucial step of biological event extraction, event trigger identification has attracted much attention and many methods have been proposed. Traditionally those methods can be categorised into rule-based approach and machine learning approach and machine learning-based approaches have demonstrated its potential and outperformed rule-based approaches in many situations. However, machine learning-based approaches still face several challenges among which a notable one is how to model semantic and syntactic information of different words and incorporate it into the prediction model. There exist many ways to model semantic and syntactic information, among which word embedding is an effective one. Therefore, in order to address this challenge, in this study, a word embedding assisted neural network prediction model is proposed to conduct event trigger identification. The experimental study on commonly used dataset has shown its potential. It is believed that this study could offer researchers insights into semantic-aware solutions for event trigger identification.

Structural information aware deep semi-supervised recurrent neural network for sentiment analysis

With the development of Internet, people are more likely to post and propagate opinions online. Sentiment analysis is then becoming an important challenge to understand the polarity beneath these comments. Currently a lot of approaches from natural language processing’s perspective have been employed to conduct this task. The widely used ones include bag-of-words and semantic oriented analysis methods. In this research, we further investigate the structural information among words, phrases and sentences within the comments to conduct the sentiment analysis. The idea is inspired by the fact that the structural information is playing important role in identifying the overall statement’s polarity. As a result a novel sentiment analysis model is proposed based on recurrent neural network, which takes the partial document as input and then the next parts to predict the sentiment label distribution rather than the next word. The proposed method learns words representation simultaneously the sentiment distribution. Experimental studies have been conducted on commonly used datasets and the results have shown its promising potential.

Auto-encoder based bagging architecture for sentiment analysis

Sentiment analysis has long been a hot topic for understanding users statements online. Previously many machine learning approaches for sentiment analysis such as simple feature-oriented SVM or more complicated probabilistic models have been proposed. Though they have demonstrated capability in polarity detection, there exist one challenge called the curse of dimensionality due to the high dimensional nature of text-based documents. In this research, inspired by the dimensionality reduction and feature extraction capability of auto-encoders, an auto-encoder-based bagging prediction architecture (AEBPA) is proposed. The experimental study on commonly used datasets has shown its potential. It is believed that this method can offer the researchers in the community further insight into bagging oriented solution for sentimental analysis. HighlightsThis research addresses the curse of dimensionality challenge by employing auto-encoders.Bagging method is employed to further improve performance.An auto-encoder-based bagging architecture is proposed and experiment on real-world datasets reveals its potential.

An Extended Technology Acceptance Model for Mobile Social Gaming Service Popularity Analysis

The games industry has been growing prosperously with the development of information technology. Recently, with further advances in social networks and mobile services, playing mobile social gaming has gradually changed our daily life in terms of social connection and leisure time spending. What are the determinant factors which affect users intention to play such games? Therefore in this research we present an empirical study on WeChat, China’s most popular mobile social network, and apply a technology acceptance model (TAM) to study the reasons beneath the popularity of games in mobile social networks. Furthermore, factors from social and mobile perspective are incorporated into the conventional TAM and their influence and relationships are studied. Experimental study on accumulated online survey data reveals several interesting findings and it is believed that this research offers the researchers in the community further insight in analysing the current popularity and future potential of mobile social games.

LSSL-SSD: Social Spammer Detection with Laplacian Score and Semi-supervised Learning

The rapid development of social networks makes it easy for people to communicate online. However, social networks usually suffer from social spammers due to their openness. Spammers deliver information for economic purposes, and they pose threats to the security of social networks. To maintain the long-term running of online social networks, many detection methods are proposed. But current methods normally use high dimension features with supervised learning algorithms to find spammers, resulting in low detection performance. To solve this problem, in this paper, we first apply the Laplacian score method, which is an unsupervised feature selection method, to obtain useful features. Based on the selected features, the semi-supervised ensemble learning is then used to train the detection model. Experimental results on the Twitter dataset show the efficiency of our approach after feature selection. Moreover, the proposed method remains high detection performance in the face of limited labeled data.

Shilling Attacks Analysis in Collaborative Filtering Based Web Service Recommendation Systems.

With the development of information technology, more and more web services have emerged, thereby making it difficult for customers to find their favorite services quickly and accurately. To overcome this difficulty, recently the collaborative filtering (CF) technique has been widely employed for personalized service recommendation, meanwhile improving the profits of service providers. Although the CF-based web service recommender systems have shown their potential, they appear to be vulnerable to shilling attack problems. Therefore, in this paper we analyze a general form of web service shilling attacks and four kinds of classical attack models, e.g., average attack, bandwagon attack, random attack, and segment attack are thoroughly investigated. Furthermore, we also study the impact of distribution-aware Pareto attack models. To demonstrate how shilling attacks alter the recommendation results, this paper analyzes 1) the variation of Quality-of-Service (QoS) prediction values of target services, 2) the QoS value prediction shifts of services with short response time which are more likely recommended, and 3) the comparison of prediction shift caused by classical attack models and Pareto attack models. The experimental results on WS-DREAM dataset revealed several interesting findings about the predictions of QoS values of target service correlated to different attack models. It is expected that this work can provide some insight for future vulnerability analysis of CF-based web service recommender systems.