In this paper, we present SSDNet, a novel deep learning approach for time series forecasting. SSDNet combines the Transformer architecture with state space models to provide probabilistic and interpretable forecasts, including trend and seasonality components and previous time steps important for the prediction. The Transformer architecture is used to learn the temporal patterns and estimate the parameters of the state space model directly and efficiently, without the need for Kalman filters. We comprehensively evaluate the performance of SSDNet on five data sets, showing that SSDNet is an effective method in terms of accuracy and speed, outperforming state-of-the-art deep learning and statistical methods, and able to provide meaningful trend and seasonality components.

The supermarkets often use sales promotions to attract customers and create brand loyalty. They would often like to know if their promotions are effective for various customers, so that better timing and more suitable rate can be planned in the future. Given a transaction data set collected by an Australian national supermarket chain, in this paper we conduct a case study aimed at discovering customers' long-term purchase patterns, which may be induced by preference changes, as well as short-term purchase patterns, which may be induced by promotions. Since purchase events of individual customers may be too sparse to model, we propose to discover a number of latent purchase patterns from the data. The latent purchase patterns are modeled via a mixture of non-homogeneous Poisson processes where each Poisson intensity function is composed by long-term and short-term components. Through the case study, 1) we validate that our model can accurately estimate the occurrences of purchase events; 2) we discover easy-to-interpret long-term gradual changes and short-term periodic changes in different customer groups; 3) we identify the customers who are receptive to promotions through the correlation between behavior patterns and the promotions, which is particularly worthwhile for target marketing.

Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant.

The reciprocal recommender is a class of recommender system that is important for several tasks where people are both the subjects and objects of the recommendation. Some examples are: job recommendation, mentor-mentee matching, and online dating. Despite the importance of this type of recommender, our work is the first to distinguish it and define its properties. We have implemented RECON, a reciprocal recommender for online dating, and have evaluated it on a large dataset from a major Australian dating website. We investigated the predictive power gained by taking account of reciprocity, finding that it is substantial, for example it improved the success rate of the top ten recommendations from 23% to 42% and also improved the recall at the same time. We also found reciprocity to help with the cold start problem obtaining a success rate of 26% for the top ten recommendations for new users. We discuss the implications of these results for broader uses of our approach for other reciprocal recommenders.

Reciprocal recommender is a class of recommender systems that is important for tasks where people are both the subject and the object of the recommendation; one such task is online dating. We have implemented RECON, a reciprocal recommender for online dating, and we have evaluated it on a major dating website. Results show an improved success rate for recommendations that consider reciprocity in comparison to recommendations that only consider the preferences of the users receiving the recommendations.

Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. The goal is to enable the groups and their facilitators to see relevant aspects of the group's operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. The context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. The results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.

When dealing with information overload from the Internet, such as the classification of Web pages and the filtering of email spam, a new technique called co-training has been shown to be a promising approach to help build more accurate classifiers. Co-training allows classifiers to learn with fewer labelled documents by taking advantage of the more abundant unclassified documents. However, conventional co-training requires the dataset to be described by two disjoint and natural feature sets that are sufficiently redundant. In many practical situations, it is not intuitively obvious how to obtain two natural feature sets. This paper shows that when only a single natural feature set is used, the performance of co-training is beneficial in the application of email classification.

We present a neural network based system for automated e-mail filing into folders and anti-spam filtering. The experiments show that it is more accurate than several other techniques. We also investigate the effects of various feature selection, weighting and normalization methods, and also the portability of the anti-spam filter across different users.

We present INTIMATE, a Web-based movie recommender that makes suggestions by using text categorization to learn from movie synopses. The performance of various feature representations, feature selectors, feature weighting mechanisms and classifiers is evaluated and discussed. INTIMATE was also compared with a feature-based movie recommender. The results show that the text-based approach outperforms the feature-based if the ratio of the number of user ratings to the vocabulary size is high.