The tourism industry is a key economic driver for many cities. To understand tourists' traveling patterns can help both public and private relevant sectors design and improve their services to serve tourists better and get additional values from it. The existing approaches to discover tourists' traveling pattern focus on small sets of known tourists extracted from social media or other channels. The accuracy of the mining result cannot be guaranteed due to the small and bias set of samples.

Tourism industry has become a key economic driver for Singapore. Understanding the behaviors of tourists is very important for the government and private sectors, e.g., restaurants, hotels and advertising companies, to improve their existing services or create new business opportunities. In this joint work with Singapore's Land Transport Authority (LTA), we innovatively apply machine learning techniques to identity the tourists among public commuters using the public transportation data provided by LTA. On successful identification, the travelling patterns of tourists are then revealed and thus allow further analyses to be carried out such as on their favorite destinations, region of stay, etc. Technically, we model the tourists identification as a classification problem, and design an iterative learning algorithm to perform inference with limited prior knowledge and labeled data. We show the superiority of our algorithm with performance evaluation and comparison with other state-of-the-art learning algorithms. Further, we build an interactive web-based system for answering queries regarding the moving patterns of the tourists, which can be used by stakeholders to gain insight into tourists' travelling behaviors in Singapore.

Advances in Web 2.0 technology has led to the rising popularity of many social network services. For example, there are over 500 million active users in Twitter. Given the huge number of users, user recommendation has gained importance where the goal is to find a set of users whom a target user is likely to follow. Content-based approaches that rely on tweet content for user recommendation have low precision as tweet contents are typically short and noisy, while collaborative filtering approaches that utilize follower-followee relationships lead to higher precision but data sparsity remains a challenge. In this work, we propose a community-based approach to user recommendation in Twitter-style social networks. Forming communities enables us to reduce data sparsity as the focus is on discover the latent characteristics of communities instead of individuals. We employ an LDA-based method on the follower-followee relationships to discover communities before applying the state-of-the-art matrix factorization method on each of the communities. This approach proves effective in improving the conversion rate (by as much as 20%) as demonstrated by the results of extensive experiments on two real world data sets Twitter and Weibo. In addition, the community-based approach is scalable as the individual community can be analyzed separately.

Given the vast amount of information on the World Wide Web, recommender systems are increasingly being used to help filter irrelevant data and suggest information that would interest users. Traditional systems make recommendations based on a single domain e.g., movie or book domain. Recent work has examined the correlations in different domains and designed models that exploit user preferences on a source domain to predict user preferences on a target domain. However, these methods are based on matrix factorization and can only be applied to two-dimensional data. Transferring high dimensional data from one domain to another requires decomposing the high dimensional data to binary relations which results in information loss.

Existing recommender systems model user interests and the social influences independently. In reality, user interests may change over time, and as the interests change, new friends may be added while old friends grow apart and the new friendships formed may cause further interests change. This complex interaction requires the joint modeling of user interest and social relationships over time. In this paper, we propose a probabilistic generative model, called Receptiveness over Time Model (RTM), to capture this interaction. We design a Gibbs sampling algorithm to learn the receptiveness and interest distributions among users over time. The results of experiments on a real world dataset demonstrate that RTM-based recommendation outperforms the state-of-the-art recommendation methods. Case studies also show that RTM is able to discover the user interest shift and receptiveness change over time

Personalized recommender systems aim to push only the relevant items and information directly to the users without requiring them to browse through millions of web resources. The challenge of these systems is to achieve a high user acceptance rate on their recommendations. In this paper, we aim to increase the user acceptance of recommendations by providing more intuitive tag-based explanations of why the items are recommended. Tags are used as intermediary entities that not only relate target users to the recommended items but also understand users' intents. Our system also allows tag-based online relevance feedback. Experiment results on the Movielens dataset show that the proposed approach is able to increase the acceptance rate of recommendations and improve user satisfaction.

The development of Web 2.0 technology has led to huge economic benefits and challenges for both e-commerce websites and online shoppers. One core technology to increase sales and consumers' satisfaction is the use of recommender systems. Existing product recommender systems consider the order of items purchased by users to obtain a list of recommended items. However, they do not consider the time interval between the products purchased. For example, there is often an interval of 2-3 months between the purchase of printer ink cartridges or refills. Thus, recommending appropriate ink cartridges one week before the user needs to replace the depleted ink cartridges would increase the likelihood of a purchase decision. In this paper, we propose to utilize the purchase interval information to improve the performance of the recommender systems for e-commerce. We design an efficient algorithm to compute the purchase intervals between product pairs from users' purchase history and integrate this information into the marginal utility model. We evaluate our approach on a real world ecommerce dataset. Experimental results demonstrate that our approach significantly improves the conversion rate and temporal diversity compared to state-of-the-art algorithms.

Social network systems such as FaceBook and YouTube have played a significant role in capturing both explicit and implicit user preferences for different items in the form of ratings and tags. This forms a quaternary relationship among users, items, tags and ratings. Existing systems have utilized only ternary relationships such as users-items-ratings, or users-items-tags to derive their recommendations. In this paper, we show that ternary relationships are insufficient to provide accurate recommendations. Instead, we model the quaternary relationship among users, items, tags and ratings as a 4-order tensor and cast the recommendation problem as a multi-way latent semantic analysis problem. A unified framework for user recommendation, item recommendation, tag recommendation and item rating prediction is proposed. The results of extensive experiments performed on a real world dataset demonstrate that our unified framework outperforms the state-of-the-art techniques in all the four recommendation tasks.