Improving Top-N recommendations with user consuming profiles

In this work, we observe that user consuming styles tend to change regularly following some profiles. Therefore, we propose a consuming profile model to capture the user consuming styles, then apply it to improve the Top-N recommendation. The basic idea is to model user consuming styles by constructing a representative subspace. Then, a set of candidate items can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results show that the proposed model can improve the accuracy of Top-N recommendations much better than the state-of-the-art algorithms.

Learning rating patterns for Top-N recommendations

Two rating patterns exist in the user × item rating matrix and influence each other: the personal rating patterns are hidden in each user's entire rating history, while the global rating patterns are hidden in the entire user × item rating matrix. In this paper, a Rating Pattern Subspace is proposed to model both of the rating patterns simultaneously by iteratively refining each other with an EM-like algorithm. Firstly, a low-rank subspace is built up to model the global rating patterns from the whole user × item rating matrix, then, the projection for each user on the subspace is refined individually based on his/her own entire rating history. After that, the refined user projections on the subspace are used to improve the modelling of the global rating patterns. Iteratively, we can obtain a well-trained low-rank Rating Pattern Subspace, which is capable of modelling both the personal and the global rating patterns. Based on this subspace, we propose a RapSVD algorithm to generate Top-N recommendations, and the experiment results show that the proposed method can significantly outperform the other state-of-the-art Top-N recommendation methods in terms of accuracy, especially on long tail item recommendations.

Learning user preference patterns for Top-N recommendations

In this paper, we observe that the user preference styles tend to change regularly following certain patterns. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation- Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N recommendation in terms of accuracy. © 2012 IEEE.

A learning method for Top-N recommendations with incomplete data

In this paper, we tackle the incompleteness of user rating history in the context of collaborative filtering for Top-N recommendations. Previous research ignore a fact that two rating patterns exist in the user × item rating matrix and influence each other. More importantly, their interactive influence characterizes the development of each other, which can consequently be exploited to improve the modelling of rating patterns, especially when the user × item rating matrix is highly incomplete due to the well-known data sparsity issue. This paper proposes a Rating Pattern Subspace to iteratively re-optimize the missing values in each user’s rating history by modelling both the global and the personal rating patterns simultaneously. The basic idea is to project the user × item rating matrix on a low-rank subspace to capture the global rating patterns. Then, the projection of each individual user on the subspace is further optimized according to his/her own rating history and the captured global rating patterns. Finally, the optimized user projections are used to improve the modelling of the global rating patterns. Based on this subspace, we propose a RapSVD-L algorithm for Top-N recommendations. In the experiments, the performance of the proposed method is compared with the state-of-the-art Top-N recommendation methods on two real datasets under various data sparsity levels. The experimental results show that RapSVD-L outperforms the compared algorithms not only on the all items recommendations but also on the long tail item recommendations in terms of accuracy.

Modelling personal preferences for Top-N movie recommendations

Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy.

Identifying interests of web users for effective recommendations

Search log data is multi dimensional data consisting of number of searches of multiple users with many searched parameters. This data can be used to identify a user’s interest in an item or object being searched. Identifying highest interests of a Web user from his search log data is a complex process. Based on a user’s previous searches, most recommendation methods employ two-dimensional models to find relevant items. Such items are then recommended to a user. Two-dimensional data models, when used to mine knowledge from such multi dimensional data may not be able to give good mappings of user and his searches. The major problem with such models is that they are unable to find the latent relationships that exist between different searched dimensions. In this research work, we utilize tensors to model the various searches made by a user. Such high dimensional data model is then used to extract the relationship between various dimensions, and find the prominent searched components. To achieve this, we have used popular tensor decomposition methods like PARAFAC, Tucker and HOSVD. All experiments and evaluation is done on real datasets, which clearly show the effectiveness of tensor models in finding prominent searched components in comparison to other widely used two-dimensional data models. Such top rated searched components are then given as recommendation to users.

Refining user and item profiles based on multidimensional data for top-n item recommendation

In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.

How the Entrepreneurial Top Management Team Setup Influences Firm Performance and the Ability to Raise Capital: A Literature Review

This paper reviews research findings on entrepreneurial top management teams within the last 20 years. It concentrates on team-based management factors and their influence on a new venture’s growth and ability to raise capital. This paper integrates recent findings and provides an overview of the current state of research. Moreover, it contributes to the overall topic by proposing five clusters of major team-specific influences, derives determinants of success and failure, and reveals recommendations for further research.

Construction project control in the UK:current practice, existing problems and recommendations for future improvement

The aim of this study is to address the main deficiencies with the prevailing project cost and time control practices for construction projects in the UK. A questionnaire survey was carried out with 250 top companies followed by in-depth interviews with 15 experienced practitioners from these companies in order to gain further insights of the identified problems, and their experience of good practice on how these problems can be tackled. On the basis of these interviews and syntheses with literature, a list of 65 good practice recommendations have been developed for the key project control tasks: planning, monitoring, reporting and analysing. The Delphi method was then used, with the participation of a panel of 8 practitioner experts, to evaluate these improvement recommendations and to establish their degree of relevance. After two rounds of Delphi, these recommendations are put forward as "critical", "important", or "helpful" measures for improving project control practice.

Technology strategy : findings from adoption and adaptation of Japanese manufacturing management (JMM) to ASEAN and Australasian automotive manufacturers

The purpose of this study is to demonstrate the appropriateness of “Japanese Manufacturing Management” (JMM) strategies in the Asian, ASEAN and Australasian automotive sectors. Secondly, the study assessed JMM as a prompt, effective and efficient global manufacturing management practice for automotive manufacturing companies to learn; benchmark for best practice; acquire product and process innovation, and enhance their capabilities and capacities. In this study, the philosophies, systems and tools that have been adopted in various automotive manufacturing assembly plants and their tier 1 suppliers in the three Regions were examined. A number of top to middle managers in these companies were located in Thailand, Indonesia, Malaysia, Singapore, Philippines, Viet Nam, and Australia and were interviewed by using a qualitative methodology. The results confirmed that the six pillars of JMM (culture change, quality at shop floor, consensus, incremental continual improvement, benchmarking, and backward-forward integration) are key enablers to success in adopting JMM in both automotive and other manufacturing sectors in the three Regions. The analysis and on-site interviews identified a number of recommendations that were validated by the automotive manufacturing company’s managers as the most functional JMM strategies.

From the account manager’s conscience to top-of-mind : account planning in Australia

What began as the “account manager’s conscience” has grown to be top-of-mind in Australian advertising today. Account planning is a hybrid discipline which uses research to bring the consumer voice to the campaign process during strategy generation, creative development and evaluation. In Australia, account planning is subjected to the “Vegemite Factor” where planners are spread too thinly across accounts and much of the market is dominated by freelance researchers and planners. This unique environment has shaped many different perceptions of account planning in Australia. These are compared with an international definition of account planning and the current research. While many basic tenants of the definition are shared by Australian advertising professionals, the difference appears to be in the ongoing nature, team approach and level of commitment. In Australia, account planners seem to be more facilitators of the strategic direction, than directors of it. Instead of exerting a sustained influence across the campaign, most energy appears to be expended at the start of campaign development, rather than extending through to its evaluation.

Improving ICT integration in today’s AEC industry: recommendations & guidelines

The Cooperative Research Centre (CRC) for Construction Innovation research project 2001-008-C: ‘Project Team Integration: Communication, Coordination and Decision Support’, is supported by a number of Australian industry, government and university based project partners including: Queensland University of Technology (QUT); Commonwealth Scientific Industrial Research Organisation (CSIRO), University of Newcastle; Queensland Department of Public Works (QDPW); and the Queensland Department of Main Roads (QDMR). Supporting the various research aims and objectives of the 2001-008-C (Part B) QUT / Industry Partner agreements, and as a major deliverable for the project, this report is not intended as a comprehensive statement of Architectural, Engineering and Contractor (AEC) industry best practice recommendations. Rather it should read as a set of research and industry recommended guidelines, based on extensive literature reviews and two years worth of investigative activities examining both public and private industry uptake of innovative information and communication technology (ICT) solutions, whilst highlighting the overall need for culture change.