Using Association Rules to Solve the Cold-Start Problem in Recommender Systems

Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus it is difficult for a recommender system to make quality recommendations. This problem is known as the cold-start problem. Here we investigate using association rules as a source of information to expand a user profile and thus avoid this problem. Our experiments show that it is possible to use association rules to noticeably improve the performance of a recommender system under the cold-start situation. Furthermore, we also show that the improvement in performance obtained can be achieved while using non-redundant rule sets. This shows that non-redundant rules do not cause a loss of information and are just as informative as a set of association rules that contain redundancy.

Road crash proneness prediction using data mining

Developing safe and sustainable road systems is a common goal in all countries. Applications to assist with road asset management and crash minimization are sought universally. This paper presents a data mining methodology using decision trees for modeling the crash proneness of road segments using available road and crash attributes. The models quantify the concept of crash proneness and demonstrate that road segments with only a few crashes have more in common with non-crash roads than roads with higher crash counts. This paper also examines ways of dealing with highly unbalanced data sets encountered in the study.

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

A fragment of the LG3 peptide of endorepellin is present in the urine of physically active mining workers : a potential marker of physical activity

Biomarker analysis has been implemented in sports research in an attempt to monitor the effects of exertion and fatigue in athletes. This study proposed that while such biomarkers may be useful for monitoring injury risk in workers, proteomic approaches might also be utilised to identify novel exertion or injury markers. We found that urinary urea and cortisol levels were significantly elevated in mining workers following a 12 hour overnight shift. These levels failed to return to baseline over 24h in the more active maintenance crew compared to truck drivers (operators) suggesting a lack of recovery between shifts. Use of a SELDI-TOF MS approach to detect novel exertion or injury markers revealed a spectral feature which was associated with workers in both work categories who were engaged in higher levels of physical activity. This feature was identified as the LG3 peptide, a C-terminal fragment of the anti-angiogenic / anti-tumourigenic protein endorepellin. This finding suggests that urinary LG3 peptide may be a biomarker of physical activity. It is also possible that the activity mediated release of LG3 / endorepellin into the circulation may represent a biological mechanism for the known inverse association between physical activity and cancer risk / survival.

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Discovering novel knowledge using granule mining

This paper presents an extended granule mining based methodology, to effectively describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other granules, it provides a kind of novel knowledge in databases. We also provide an algorithm to implement the proposed methodology. The experiments conducted to characterize a real network traffic data collection show that the proposed concepts and algorithm are promising.

Mining work, family and community : a spatially-oriented approach to the impact of the Ravensthorpe nickel mine closure in remote Australia

While changes in work and employment practices in the mining sector have been profound, the literature addressing mining work is somewhat partial as it focuses primarily on the workplace as the key (or only) site of analysis, leaving the relationship between mining work and families and communities under-theorized. This article adopts a spatially oriented, case-study approach to the sudden closure of the Ravensthorpe nickel mine in the south-west of Western Australia to explore the interplay between the new scales and mobilities of labour and capital and work–family–community connections in mining. In the context of the dramatically reconfigured industrial arena of mining work, the study contributes to a theoretical engagement between employment relations and the spatial dimensions of family and community in resource-affected communities.

Granule mining and its application for network traffic characterization

Decision table and decision rules play an important role in rough set based data analysis, which compress databases into granules and describe the associations between granules. Granule mining was also proposed to interpret decision rules in terms of association rules and multi-tier structure. In this paper, we further extend granule mining to describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other ganules, it provides a kind of novel knowledge in databases. Some experiments are conducted to test the proposed new concepts for describing the characteristics of a real network traffic data collection. The results show that the proposed concepts are promising.

A data mining driven risk profiling method for road asset management

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach, which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this chapter we propose two approaches which measure multi-level association rules to help evaluate their interestingness by considering the database’s underlying taxonomy. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Structured feature extraction using association rules

As of today, opinion mining has been widely used to iden- tify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feed- back such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hi- erarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be ap- plied to generate features about a service such as the services in hospitals or clinics.

Oscar Pistorius and the death of Reeva Steenkamp : mapping public opinions on Twitter using frame analysis and data mining

This paper uses innovative content analysis techniques to map how the death of Oscar Pistorius' girlfriend, Reeva Steenkamp, was framed on Twitter conversations. Around 1.5 million posts from a two-week timeframe are analyzed with a combination of syntactic and semantic methods. This analysis is grounded in the frame analysis perspective and is different than sentiment analysis. Instead of looking for explicit evaluations, such as “he is guilty” or “he is innocent”, we showcase through the results how opinions can be identified by complex articulations of more implicit symbolic devices such as examples and metaphors repeatedly mentioned. Different frames are adopted by users as more information about the case is revealed: from a more episodic one, highly used in the very beginning, to more systemic approaches, highlighting the association of the event with urban violence, gun control issues, and violence against women. A detailed timeline of the discussions is provided.

The Australian mining industry and the ideal mining woman : mobilizing a public business case for gender equality

Despite ongoing ‘boom’ conditions in the Australian mining industry, women remain substantially and unevenly under-represented in the sector, as is the case in other resource-dependent countries. Building on the literature critiquing business-case rationales and strategies as a means to achieve women’s equality in the workplace, we examine the business case for employing more women as advanced by the Australian mining industry. Specifically, we apply a discourse analysis to seven substantial, publically-available documents produced by the industry’s national and state peak organizations between 2005 and 2013. Our study makes two contributions. First, we map the features of the business case at the sectoral rather than firm or workplace level and examine its public mobilization. Second, we identify the construction and deployment of a normative identity – ‘the ideal mining woman’ – as a key outcome of this business-case discourse. Crucially, women are therein positioned as individually responsible for gender equality in the workplace.

Interpretation of association rules in multi-tier structures

Dealing with the large amount of data resulting from association rule mining is a big challenge. The essential issue is how to provide efficient methods for summarizing and representing meaningful discovered knowledge from databases. This paper presents a new approach called multi-tier granule mining to improve the performance of association rule mining. Rather than using patterns, it uses granules to represent knowledge that is implicitly contained in relational databases. This approach also uses multi-tier structures and association mappings to interpret association rules in terms of granules. Consequently, association rules can be quickly assessed and meaningless association rules can be justified according to these association mappings. The experimental results indicate that the proposed approach is promising