Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

A framework for personalized dynamic cross-selling in e-commerce retailing

Cross-selling and product bundling are prevalent strategies in the retail sector. Instead of static bundling offers, i.e. giving the same offer to everyone, personalized dynamic cross-selling generates targeted bundle offers and can help maximize revenues and profits. In resolving the two basic problems of dynamic cross-selling, which involves selecting the right complementary products and optimizing the discount, the issue of computational complexity becomes central as the customer base and length of the product list grows. Traditional recommender systems are built upon simple collaborative filtering techniques, which exploit the informational cues gained from users in the form of product ratings and rating differences across users. The retail setting differs in that there are only records of transactions (in period X, customer Y purchased product Z). Instead of a range of explicit rating scores, transactions form binary datasets; 1-purchased and 0-not-purchased. This makes it a one-class collaborative filtering (OCCF) problem. Notwithstanding the existence of wider application domains of such an OCCF problem, very little work has been done in the retail setting. This research addresses this gap by developing an effective framework for dynamic cross-selling for online retailing. In the first part of the research, we propose an effective yet intuitive approach to integrate temporal information regarding a product\u27s lifecycle (i.e., the non-stationary nature of the sales history) in the form of a weight component into latent-factor-based OCCF models, improving the quality of personalized product recommendations. To improve the scalability of large product catalogs with transaction sparsity typical in online retailing, the approach relies on product catalog hierarchy and segments (rather than individual SKUs) for collaborative filtering. In the second part of the work, we propose effective bundle discount policies, which estimate a specific customer\u27s interest in potential cross-selling products (identified using the proposed OCCF methods) and calibrate the discount to strike an effective balance between the probability of the offer acceptance and the size of the discount. We also developed a highly effective simulation platform for generation of e-retailer transactions under various settings and test and validate the proposed methods. To the best of our knowledge, this is the first study to address the topic of real-time personalized dynamic cross-selling with discounting. The proposed techniques are applicable to cross-selling, up-selling, and personalized and targeted selling within the e-retail business domain. Through extensive analysis of various market scenario setups, we also provide a number of managerial insights on the performance of cross-selling strategies

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

MATRIX DECOMPOSITION FOR DATA DISCLOSURE CONTROL AND DATA MINING APPLICATIONS

Access to huge amounts of various data with private information brings out a dual demand for preservation of data privacy and correctness of knowledge discovery, which are two apparently contradictory tasks. Low-rank approximations generated by matrix decompositions are a fundamental element in this dissertation for the privacy preserving data mining (PPDM) applications. Two categories of PPDM are studied: data value hiding (DVH) and data pattern hiding (DPH). A matrix-decomposition-based framework is designed to incorporate matrix decomposition techniques into data preprocessing to distort original data sets. With respect to the challenge in the DVH, how to protect sensitive/confidential attribute values without jeopardizing underlying data patterns, we propose singular value decomposition (SVD)-based and nonnegative matrix factorization (NMF)-based models. Some discussion on data distortion and data utility metrics is presented. Our experimental results on benchmark data sets demonstrate that our proposed models have potential for outperforming standard data perturbation models regarding the balance between data privacy and data utility. Based on an equivalence between the NMF and K-means clustering, a simultaneous data value and pattern hiding strategy is developed for data mining activities using K-means clustering. Three schemes are designed to make a slight alteration on submatrices such that user-specified cluster properties of data subjects are hidden. Performance evaluation demonstrates the efficacy of the proposed strategy since some optimal solutions can be computed with zero side effects on nonconfidential memberships. Accordingly, the protection of privacy is simplified by one modified data set with enhanced performance by this dual privacy protection. In addition, an improved incremental SVD-updating algorithm is applied to speed up the real-time performance of the SVD-based model for frequent data updates. The performance and effectiveness of the improved algorithm have been examined on synthetic and real data sets. Experimental results indicate that the introduction of the incremental matrix decomposition produces a significant speedup. It also provides potential support for the use of the SVD technique in the On-Line Analytical Processing for business data analysis

Journal of Telecommunications and Information Technology, nr 4

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