Open Access. Powered by Scholars. Published by Universities.®

Databases and Information Systems Commons™

Open Access. Powered by Scholars. Published by Universities.®

Singapore Management University

Discipline
Keyword
Publication Year
Publication
Publication Type

Articles 1801 - 1830 of 3560

Full-Text Articles in Databases and Information Systems

Lexicons In Sentiment Analytics, B. Yuan, Keng Siau May 2017

Lexicons In Sentiment Analytics, B. Yuan, Keng Siau

Research Collection School Of Computing and Information Systems

With the increasing amount of text data, sentiment analytics (SA) is becoming an important tool for text miners. An automated approach is needed to parse the online reviews and comments, and analyze their sentiments. Since lexicon is the most important component in SA, enhancing the quality of lexicons will improve the efficiency and accuracy of sentiment analysis. In this research, we study the effect of coupling a general lexicon with a specialized lexicon (for a specific domain) and its impact on sentiment analysis. Two special domains and one general domain were used. The two special domains are the petroleum domain …

Go to article

Machine Learning Approaches To Sentiment Analytics, W. Zhao, Keng Siau May 2017

Machine Learning Approaches To Sentiment Analytics, W. Zhao, Keng Siau

Research Collection School Of Computing and Information Systems

One key aspect of sentiment analytics is emotion classification. This research studies the use of machine learning approaches to classify human emotion. Two different machine learning approaches were compared in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, genders were separated and two separate machines were used to learn the emotions of the two genders. We also manipulated the training sample sizes and study the effect of training sample sizes on the two machine learning approaches. Our preliminary results show that the approach where the genders were separated produces …

Go to article

Joint Optimization Of Resource Provisioning In Cloud Computing, Jonathan David Chase, Dusit Niyato May 2017

Joint Optimization Of Resource Provisioning In Cloud Computing, Jonathan David Chase, Dusit Niyato

Research Collection School Of Computing and Information Systems

Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over-and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst …

Go to article

Data-Driven Approach To Measuring The Level Of Press Freedom Using Media Attention Diversity From Unfiltered News, Jisun An, Haewoon Kwak May 2017

Data-Driven Approach To Measuring The Level Of Press Freedom Using Media Attention Diversity From Unfiltered News, Jisun An, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Published by Reporters Without Borders every year, the Press Freedom Index (PFI) reflects the fear and tension in the newsroom pushed by the government and private sectors. While the PFI is invaluable in monitoring media environ- ments worldwide, the current survey-based method has in- herent limitations to updates in terms of cost and time. In this work, we introduce an alternative way to measure the level of press freedom using media attention diversity compiled from Unfiltered News.

Go to article

Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen May 2017

Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We develop a methodology for persona generation using real time social media data for the distribution of products via online platforms. From a large social media account containing more than 30 million interactions from users from 181 countries engaging with more than 4,200 digital products produced by a global media corporation, we demonstrate that our methodology can first identify both distinct and impactful user segments and then create persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We validate our approach by implementing the methodology into an actual working system that leverages large scale online …

Go to article

Robust Object Tracking Via Locality Sensitive Histograms, Shengfeng He, Rynson W.H Lau, Qingxiong Yang, Jiang Wang, Ming-Hsuan Yang May 2017

Robust Object Tracking Via Locality Sensitive Histograms, Shengfeng He, Rynson W.H Lau, Qingxiong Yang, Jiang Wang, Ming-Hsuan Yang

Research Collection School Of Computing and Information Systems

This paper presents a novel locality sensitive histogram (LSH) algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrence of each intensity value by adding ones to the corresponding bin, an LSH is computed at each pixel location, and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value exponentially reduces with respect to the distance to the pixel location where the histogram is computed. An efficient algorithm is proposed that enables the LSHs to be computed in time linear in the image size and the …

Go to article

Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li May 2017

Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li

Research Collection School Of Computing and Information Systems

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. …

Go to article

Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim May 2017

Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse for building models. Nonetheless, although only a small fraction of tweets are geocoded, we find that they are posted by a substantial proportion of users. Essentially, such users have location history data. Along with tweet posting time, these serve as additional contextual information for geolocation. In designing our geolocation models, we …

Go to article

A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim May 2017

A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim

Research Collection School Of Computing and Information Systems

This study proposes adata-driven approach for benchmarking energy efficiency of warehouse buildings.Our proposed approach provides an alternative to the limitation of existingbenchmarking approaches where a theoretical energy-efficient warehouse was usedas a reference. Our approach starts by defining the questions needed to capturethe characteristics of warehouses relating to energy consumption. Using an existingdata set of warehouse building containing various attributes, we first cluster theminto groups by their characteristics. The warehouses characteristics derivedfrom the cluster assignments along with their past annual energy consumptionare subsequently used to train a decision tree model. The decision tree providesa classification of what factors contribute to different …

Go to article

Encrypted Data Processing With Homomorphic Re-Encryption, Wenxiu Ding, Zheng Yan, Robert H. Deng May 2017

Encrypted Data Processing With Homomorphic Re-Encryption, Wenxiu Ding, Zheng Yan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cloud computing offers various services to users by re-arranging storage and computing resources. In order to preserve data privacy, cloud users may choose to upload encrypted data rather than raw data to the cloud. However, processing and analyzing encrypted data are challenging problems, which have received increasing attention in recent years. Homomorphic Encryption (HE) was proposed to support computation on encrypted data and ensure data confidentiality simultaneously. However, a limitation of HE is it is a single user system, which means it only allows the party that owns a homomorphic decryption key to decrypt processed ciphertexts. Original HE cannot support …

Go to article

Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun May 2017

Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun

Research Collection School Of Computing and Information Systems

Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has gained increasing success in many applications. Despite enjoying many advantages, the existing Batch Decoupled Inference algorithm for the CTR model has some critical limitations: First of all, it is designed to work in a batch learning manner, making it unsuitable to deal with streaming data or big data in real-world recommender systems. Secondly, in the existing algorithm, the item-specific topic proportions of LDA are fed to the downstream PMF but the rating information is not exploited in discovering …

Go to article

A Neural Network Model For Semi-Supervised Review Aspect Identification, Ying Ding, Changlong Yu, Jing Jiang May 2017

A Neural Network Model For Semi-Supervised Review Aspect Identification, Ying Ding, Changlong Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Aspect identification is an important problem in opinion mining. It is usually solved in an unsupervised manner, and topic models have been widely used for the task. In this work, we propose a neural network model to identify aspects from reviews by learning their distributional vectors. A key difference of our neural network model from topic models is that we do not use multinomial word distributions but instead embedding vectors to generate words. Furthermore, to leverage review sentences labeled with aspect words, a sequence labeler based on Recurrent Neural Networks (RNNs) is incorporated into our neural network. The resulting model …

Go to article

Determining The Impact Regions Of Competing Options In Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung. Yiu May 2017

Determining The Impact Regions Of Competing Options In Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung. Yiu

Research Collection School Of Computing and Information Systems

In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a weight vector. The score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting, the user input is a vector (equivalently, a point) in a d-dimensional preference space, where d is the number of data attributes. In this paper we study the problem of determining in which regions of the preference space the weight vector should …

Go to article

Provably Secure Attribute Based Signcryption With Delegated Computation And Efficient Key Updating, Hanshu Hong, Yunhao Xia, Zhixin Sun, Ximeng Liu May 2017

Provably Secure Attribute Based Signcryption With Delegated Computation And Efficient Key Updating, Hanshu Hong, Yunhao Xia, Zhixin Sun, Ximeng Liu

Research Collection School Of Computing and Information Systems

Equipped with the advantages of flexible access control and fine-grained authentication, attribute based signcryption is diffusely designed for security preservation in many scenarios. However, realizing efficient key evolution and reducing the calculation costs are two challenges which should be given full consideration in attribute based cryptosystem. In this paper, we present a key-policy attribute based signcryption scheme (KP-ABSC) with delegated computation and efficient key updating. In our scheme, an access structure is embedded into user’s private key, while ciphertexts corresponds a target attribute set. Only the two are matched can a user decrypt and verify the ciphertexts. When the access …

Go to article

Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing, Truc Viet Le, Baoyang Song, Laura Wynter May 2017

Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing, Truc Viet Le, Baoyang Song, Laura Wynter

Research Collection School Of Computing and Information Systems

The proliferation of wireless technologies in today's everyday life is one of the key drivers of the Internet of Things (IoT). In addition to being an enabler of connectivity, the vast penetration of wireless devices today gives rise to a secondary functionality as a means of tracking and localization of the devices themselves. Indeed, in order to discover and automatically connect to known Wi-Fi networks, mobile devices have to scan and broadcast the so-called probe requests on all available channels, which can be captured and analyzed in a non-intrusive manner. Thus, one of the key applications of this feature is …

Go to article

Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing, Truc Viet Le, Baoyang Song, Laura Wynter May 2017

Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing, Truc Viet Le, Baoyang Song, Laura Wynter

Research Collection School Of Computing and Information Systems

The proliferation of wireless technologies in today's everyday life is one of the key drivers of the Internet of Things (IoT). In addition to being an enabler of connectivity, the vast penetration of wireless devices today gives rise to a secondary functionality as a means of tracking and localization of the devices themselves. Indeed, in order to discover and automatically connect to known Wi-Fi networks, mobile devices have to scan and broadcast the so-called probe requests on all available channels, which can be captured and analyzed in a non-intrusive manner. Thus, one of the key applications of this feature is …

Go to article

Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang May 2017

Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang

Research Collection School Of Computing and Information Systems

Social influence has attracted significant attention owing to the prevalence of social networks (SNs). In this paper, we study a new social influence problem, called personalized social influential tags exploration (PITEX), to help any user in the SN explore how she influences the network. Given a target user, it finds a size-k tag set that maximizes this user’s social influence. We prove the problem is NP-hard to be approximated within any constant ratio. To solve it, we introduce a sampling-based framework, which has an approximation ratio of 1−ǫ 1+ǫ with high probabilistic guarantee. To speedup the computation, we devise more …

Go to article

Dynamic Nearest Neighbor Queries In Euclidean Space, Sarana Nutanong, Mohammed Eunus Ali, Egemen Tanin, Kyriakos Mouratidis May 2017

Dynamic Nearest Neighbor Queries In Euclidean Space, Sarana Nutanong, Mohammed Eunus Ali, Egemen Tanin, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L2norm. One important variant of this query type is kNN query, which returns k data points with the minimum distances. When taking the temporal dimension into account, the k NN query result may change over a period of time due to changes in locations of the query point and/or data points.

Go to article

Design And Implementation Of An Rfid-Based Customer Shopping Behavior Mining System, Zimu Zhou, Longfei Shangguan, Xiaolong Zheng, Lei Yang, Yunhao Liu Apr 2017

Design And Implementation Of An Rfid-Based Customer Shopping Behavior Mining System, Zimu Zhou, Longfei Shangguan, Xiaolong Zheng, Lei Yang, Yunhao Liu

Research Collection School Of Computing and Information Systems

Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate …

Go to article

Factored Similarity Models With Social Trust For Top-N Item Recommendation, Guibing Guo, Jie Zhang, Feida Zhu, Xingwei Wang Apr 2017

Factored Similarity Models With Social Trust For Top-N Item Recommendation, Guibing Guo, Jie Zhang, Feida Zhu, Xingwei Wang

Research Collection School of Computing and Information Systems

Trust-aware recommender systems have attracted much attention recently due to the prevalence of social networks. However, most existing trust-based approaches are designed for the recommendation task of rating prediction. Only few trust-aware methods have attempted to recommend users an ordered list of interesting items, i.e., item recommendation. In this article, we propose three factored similarity models with the incorporation of social trust for item recommendation based on implicit user feedback. Specifically, we introduce a matrix factorization technique to recover user preferences between rated items and unrated ones in the light of both user-user and item-item similarities. In addition, we claim …

Go to article

Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua Apr 2017

Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback.Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in …

Go to article

Achievement And Friends: Key Factors Of Player Retention Vary Across Player Levels In Online Multiplayer Games, Korea Advanced Institute Of Science & Technology, Qatar Computing Research Institute, Haewoon Kwak Apr 2017

Achievement And Friends: Key Factors Of Player Retention Vary Across Player Levels In Online Multiplayer Games, Korea Advanced Institute Of Science & Technology, Qatar Computing Research Institute, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Retaining players over an extended period of time is a long-standing challenge in game industry. Significant effort has been paid to understanding what motivates players enjoy games. While individuals may have varying reasons to play or abandon a game at different stages within the game, previous studies have looked at the retention problem from a snapshot view. This study, by analyzing in-game logs of 51,104 distinct individuals in an online multiplayer game, uniquely offers a multifaceted view of the retention problem over the players' virtual life phases. We find that key indicators of longevity change with the game level. Achievement …

Go to article

I Would Not Plant Apple Trees If The World Will Be Wiped: Analyzing Hundreds Of Millions Of Behavioral Records Of Players During An Mmorpg Beta Test, Qatar Computing Research Institute, The State University Of New York University At Buffalo, Haewoon Kwak, Korea University Apr 2017

I Would Not Plant Apple Trees If The World Will Be Wiped: Analyzing Hundreds Of Millions Of Behavioral Records Of Players During An Mmorpg Beta Test, Qatar Computing Research Institute, The State University Of New York University At Buffalo, Haewoon Kwak, Korea University

Research Collection School Of Computing and Information Systems

In this work, we use player behavior during the closed beta test of the MMORPG ArcheAge as a proxy for an extreme situation: at the end of the closed beta test, all user data is deleted, and thus, the outcome (or penalty) of players' in-game behaviors in the last few days loses its meaning. We analyzed 270 million records of player behavior in the 4th closed beta test of ArcheAge. Our findings show that there are no apparent pandemic behavior changes, but some outlierswere more likely to exhibit anti-social behavior (e.g., player killing). We also found that contrary to the …

Go to article

Finding Causality And Responsibility For Probabilistic Reverse Skyline Query Non-Answers [Extended Abstract], Yunjun Gao, Qing Liu, Gang Chen, Linlin Zhou, Baihua Zheng Apr 2017

Finding Causality And Responsibility For Probabilistic Reverse Skyline Query Non-Answers [Extended Abstract], Yunjun Gao, Qing Liu, Gang Chen, Linlin Zhou, Baihua Zheng

Research Collection School Of Computing and Information Systems

This paper explores the causality and responsibility problem (CRP) for the non-answers to probabilistic reverse skyline queries (PRSQ). Towards this, we propose an efficient algorithm called CP to compute the causality and responsibility for the non-answers to PRSQ. CP first finds candidate causes, and then, it performs verification to obtain actual causes with their responsibilities, during which several strategies are used to boost efficiency. Extensive experiments using both real and synthetic data sets demonstrate the effectiveness and efficiency of the presented algorithms.

Go to article

Online Growing Neural Gas For Anomaly Detection In Changing Surveillance Scenes, Qianru Sun, Hong Liu, Tatsuya Harada Apr 2017

Online Growing Neural Gas For Anomaly Detection In Changing Surveillance Scenes, Qianru Sun, Hong Liu, Tatsuya Harada

Research Collection School Of Computing and Information Systems

Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning. Unlike a parameter-fixed GNG, our model updates learning parameters continuously, for which we propose several online neighbor-related strategies. Specific operations, namely neuron insertion, deletion, learning rate adaptation and stopping criteria selection, get upgraded to online modes. In the anomaly detection stage, the …

Go to article

On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee Peng Lim Apr 2017

On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as …

Go to article

Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim Apr 2017

Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Social media, as a major platform to disseminate information, has changed the way users and communities contribute content. In this paper, we aim to study content modifications on public Facebook pages operated by news media, community groups, and bloggers. We also study the possible reasons behind them, and their effects on user interaction. We conducted a detailed study of Content Censorship (CC) and Content Edit (CE) in Facebook using a detailed longitudinal dataset consisting of 57 public Facebook pages over 3 weeks covering 145,955 posts and 9,379,200 comments. We detected many CC and CE activities between 28% and 56% of …

Go to article

A Compare-Aggregate Model For Matching Text Sequences, Shuohang Wang, Jing Jiang Apr 2017

A Compare-Aggregate Model For Matching Text Sequences, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a general "compare-aggregate" framework that performs word-level matching followed by aggregation using Convolutional Neural Networks. We particularly focus on the different comparison functions we can use to match two vectors. We use four different datasets to evaluate the model. We find that some simple comparison functions based on element-wise operations can work better than standard neural network and neural tensor network.

Go to article

Machine Comprehension Using Match-Lstm And Answer Pointer, Shuohang Wang, Jing Jiang Apr 2017

Machine Comprehension Using Match-Lstm And Answer Pointer, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, in SQuAD the answers do not come from a small set of candidate answers and they have variable lengths. We propose an end-to-end neural architecture for the task. The architecture is based on match-LSTM, a model we proposed previously for textual entailment, and Pointer Net, a sequence-to-sequence …

Go to article

Learning Personalized Preference Of Strong And Weak Ties For Social Recommendation, Xin Wang, Steven C. H. Hoi, Martin Ester, Jiajun Bu, Chun Chen Apr 2017

Learning Personalized Preference Of Strong And Weak Ties For Social Recommendation, Xin Wang, Steven C. H. Hoi, Martin Ester, Jiajun Bu, Chun Chen

Research Collection School Of Computing and Information Systems

Recent years have seen a surge of research on social recommendation techniques for improving recommender systems due to the growing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despite the extensive studies, no existing work has attempted to distinguish and learn the personalized preferences between strong and weak ties, two important terms widely used in social sciences, for each individual in social recommendation. In this paper, we first highlight the importance of different types of ties in …

Go to article

First Prev Next Last