The role of social media data in operations and production management

Social media data contain rich information in posts or comments written by customers. If those data can be extracted and analysed properly, companies can fully utilise this rich source of information. They can then convert the data to useful information or knowledge, which can help to formulate their business strategy. This cannot only facilitate marketing research in view of customer behaviour, but can also aid other management disciplines. Operations management (OM) research and practice with the objective to make decisions on product and process design is a fine example. Nevertheless, this line of thought is under-researched. In this connection, this paper explores the role of social media data in OM research. A structured approach is proposed, which involves the analysis of social media comments and a statistical cluster analysis to identify the interrelationships amongst important factors. A real-life example is employed to demonstrate the concept

Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System

The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations

A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system

© 2015 Elsevier B.V.All rights reserved. Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services

Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

k-NN 검색 및 k-NN 그래프 생성을 위한 고속 근사 알고리즘

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 이상구.Finding k-nearest neighbors (k-NN) is an essential part of recommeder systems, information retrieval, and many data mining and machine learning algorithms. However, there are two main problems in finding k-nearest neighbors: 1) Existing approaches require a huge amount of time when the number of objects or dimensions is scale up. 2) The k-NN computation methods do not show the consistent performance over different search tasks and types of data. In this dissertation, we present fast and versatile algorithms for finding k-nearest neighbors in order to cope with these problems. The main contributions are summarized as follows: first, we present an efficient and scalable algorithm for finding an approximate k-NN graph by filtering node pairs whose large value dimensions do not match at all. Second, a fast collaborative filtering algorithm that utilizes k-NN graph is presented. The main idea of this approach is to reverse the process of finding k-nearest neighbors in item-based collaborative filtering. Last, we propose a fast approximate algorithm for k-NN search by selecting query-specific signatures from a signature pool to pick high-quality k-NN candidates.The experimental results show that the proposed algorithms guarantee a high level of accuracy while also being much faster than the other algorithms over different types of search tasks and datasets.Abstract i Contents iii List of Figures vii List of Tables xi Chapter 1 Introduction 1 1.1 Motivation and Challenges . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Fast Approximation . . . . . . . . . . . . . . . . . . . . . 3 1.1.2 Versatility . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Our Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.1 Greedy Filtering . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 Signature Selection LSH . . . . . . . . . . . . . . . . . . . 7 1.2.3 Reversed CF . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Chapter 2 Background and Related Work 14 2.1 k-NN Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.1.1 Locality Sensitive Hashing . . . . . . . . . . . . . . . . . . 15 2.1.2 LSH-based k-NN Search . . . . . . . . . . . . . . . . . . . 16 2.2 k-NN Graph Construction . . . . . . . . . . . . . . . . . . . . . . 17 2.2.1 LSH-based Approach . . . . . . . . . . . . . . . . . . . . . 19 2.2.2 Clustering-based Approach . . . . . . . . . . . . . . . . . 19 2.2.3 Heuristic-based Approach . . . . . . . . . . . . . . . . . . 20 2.2.4 Similarity Join . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Chapter 3 Fast Approximate k-NN Graph Construction 26 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3 Constructing a k-Nearest Neighbor Graph . . . . . . . . . . . . . 29 3.3.1 Greedy Filtering . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.2 Prefix Selection Scheme . . . . . . . . . . . . . . . . . . . 32 3.3.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.4.2 Graph Construction Time . . . . . . . . . . . . . . . . . . 39 3.4.3 Graph Accuracy . . . . . . . . . . . . . . . . . . . . . . . 40 3.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . 44 3.5.2 Performance Comparison . . . . . . . . . . . . . . . . . . 48 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Chapter 4 Fast Collaborative Filtering 53 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3 Fast Collaborative Filtering . . . . . . . . . . . . . . . . . . . . . 58 4.3.1 Nearest Neighbor Graph Construction . . . . . . . . . . . 58 4.3.2 Fast Recommendation Algorithm . . . . . . . . . . . . . . 60 4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . 64 4.4.2 Overall Comparison . . . . . . . . . . . . . . . . . . . . . 65 4.4.3 Effects of Parameter Changes . . . . . . . . . . . . . . . . 68 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Chapter 5 Fast Approximate k-NN Search 72 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2 Signature Selection LSH . . . . . . . . . . . . . . . . . . . . . . . 74 5.2.1 Data-dependent LSH . . . . . . . . . . . . . . . . . . . . . 75 5.2.2 Signature Pool Generation . . . . . . . . . . . . . . . . . . 76 5.2.3 Signature Selection . . . . . . . . . . . . . . . . . . . . . . 79 5.2.4 Optimization Techniques . . . . . . . . . . . . . . . . . . 83 5.3 S2LSH for Graph Construction . . . . . . . . . . . . . . . . . . . 84 5.3.1 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . 84 5.3.2 Signature Selection . . . . . . . . . . . . . . . . . . . . . . 84 5.3.3 Optimization Techniques . . . . . . . . . . . . . . . . . . 85 5.4 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . 87 5.5.2 Experimental Results . . . . . . . . . . . . . . . . . . . . 91 5.5.3 Performance Analysis . . . . . . . . . . . . . . . . . . . . 97 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 Chapter 6 Conclusion 103 Bibliography 105 초록 113Docto