Analysing and learning from sequentially ordered symbolic data is increasingly important in applications such as finance and biology. Finding patterns that show interesting behaviour, such as regularly repeating occurrences within a time interval, can provide useful insight. In this paper we address the problem of efficiently identifying such behaviours. Existing approaches often require a target period to be specified, which will limit possible patterns to those approximating the specified periodicity, such as daily, monthly, quarterly and so on. In this paper we extend one such approach, derived from frequent pattern mining, to operate without the need for user-specified periodicity. Our new algorithms can identify the time interval and periodicity, or frequency of occurrence, of all periodically occurring patterns within a certain used-specified tolerance. Experimental results of our implementation show that the new approach can identify many more patterns in a real-world financial dataset, while on other sequential datasets it finds similar numbers of patterns without significant reduction in efficiency compared to existing approaches. We also verify the algorithm’s ability to recover recurring patterns in controlled experiments on synthetic data.

We present a statistical framework to extract information-rich citation sentences that summarise the main contributions of a scientific paper. In a first stage, we automatically discover salient keywords from a paper’s citation summary, keywords that characterise its main contributions. In asecond stage, exploitingthe results of the first stage, we identify citation sentences that best capture the paper’s main contributions. Experimental results show that our approach using methods rooted in quantitative statistics and information theory outperforms the current state-of-the-art systems in scientific paper summarisation.

This paper reports on the successful deployment of a peopleto-people recommender system in a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that key metrics generally hold their value or show an increase compared to the trial results, and that the recommender system delivered its projected benefits.

This paper shows how to improve the recommendations of an interaction-based collaborative filtering (IBCF) recommender used in online dating. Previous work has shown that IBCF works well in this domain, although it tends to rank popular candidates highly, which leads to these users receiving a large number of contacts. We address this problem by using a Decision Tree model as a “critic” to re-rank the candidates generated by IBCF, effectively promoting less popular candidates. This method was first evaluated on historical data from a large online dating site and then trialled live on the same site by providing recommendations to a large number of users throughout a 9 week period. The live trial confirmed the consistency of the analysis on historical data and the ability of the method to generate suitable candidates over an extended period. Our recommendations gave higher success rates than those for a control group made with a baseline recommender.

Predicting people who other people may like has recently become an important task in many online social networks. Traditional collaborative filtering (CF) approaches are popular in recommender systems to effectively predict user preferences for items. One major problem in CF is computing similarity between users or items. Traditional CF methods often use heuristic methods to combine the ratings given to an item by similar users, which may not reflect the characteristics of the active user and can give unsatisfactory performance. In contrast to heuristic approaches we have developed CollabNet, a novel algorithm that uses gradient descent to learn the relative contributions of similar users or items to the ranking of recommendations produced by a recommender system, using weights to represent the contributions of similar users for each active user. We have applied CollabNet to the challenging problem of people to people recommendation in social networks, where people have a dual role as both "users" and "items", e.g., both initiating and receiving communications, to recommend other users to a given user, based on user similarity in terms of both taste (whom they like) and attractiveness (who likes them). Evaluation of CollabNet recommendations on datasets from a commercial online social network shows improved performance over standard CF.

This paper presents a new approach to building classifiers for Chinese character recognition. A new knowledge acquisition technique is used for building incrementally a knowledge based system for the classification task. The new knowledge acquisition technique is based on ripple down rules, which is an effective method for building large knowledge bases. We extended ripple down rules to be able to acquire graphical knowledge. In this paper a Hough transform technique is used for extracting the features which are being used for a knowledge based system. A prototype has been implemented and initial experimental results are promising.