An efficient tagging data interpretation and representation scheme for item recommendation

A tag-based item recommendation method generates an ordered list of items, likely interesting to a particular user, using the users past tagging behaviour. However, the users tagging behaviour varies in different tagging systems. A potential problem in generating quality recommendation is how to build user profiles, that interprets user behaviour to be effectively used, in recommendation models. Generally, the recommendation methods are made to work with specific types of user profiles, and may not work well with different datasets. In this paper, we investigate several tagging data interpretation and representation schemes that can lead to building an effective user profile. We discuss the various benefits a scheme brings to a recommendation method by highlighting the representative features of user tagging behaviours on a specific dataset. Empirical analysis shows that each interpretation scheme forms a distinct data representation which eventually affects the recommendation result. Results on various datasets show that an interpretation scheme should be selected based on the dominant usage in the tagging data (i.e. either higher amount of tags or higher amount of items present). The usage represents the characteristic of user tagging behaviour in the system. The results also demonstrate how the scheme is able to address the cold-start user problem

소셜 카탈로깅 서비스에서의 감정 기반 아이템 추천 기법

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 김형주.Social cataloging services allow users to catalog items, express subjective opinions, and communicate with other users. Users in social cataloging services can refer to others activities and opinions and obtain complementary information about items through the relationships with others. However, unlike a general social networking service where user behaviors are based on the connections between users, users in social cataloging services can participate and contribute to services and can obtain the information about items without links. In contrast to a general social networking service in which actions are performed based on connections between users, You can participate and contribute. In this doctoral dissertation, we classify users into two groups as connected users and isolated users and analyze usersbehaviors. Considering the characteristics of users who mainly focus on contents rather than relationships, we propose a tag emotion-based item recommendation scheme. Tags are the additional information about the item, and at the same time, it is a subjective estimation of users for items, which contains the users feelings and opinions on the item. Therefore, if we consider the emotions contained in tags, it is possible to obtain the recommendation result reflecting the users preferences or interest. In order to reflect the emotions of each tag, the ternary relationships between users, items, and tags are modeled by the three-order tensor, and new items are recommended based on the latent semantic information derived by a high order singular value decomposition technique. However, the data sparsity problem occurs because the number of items in which a user is tagged is smaller than the amount of all items. In addition, since the recommendation is based on the latent semantic information among users, items, and tags, the previous tagging histories of users and items are not considered. Therefore, in this dissertation, we use item-based collaborative filtering technique to generate additional data to build an extended data set. We also propose an improved recommendation method considering the user and item profiles. The proposed method is evaluated based on the actual data of social cataloging service. As a result, we show that the proposed method improves the recommendation performances compared to the collaborative filtering and other tensor-based recommendation methods.Chapter 1 Introduction 1 1.1 Research Motivation 1 1.2 Research Contributions 3 1.3 Dissertation Outline 5 Chapter 2 Backgrounds and Related Work 7 2.1 Online Social Networks and Social Cataloging Services 7 2.2 Terminologies 9 2.3 Related Work 12 2.3.1 Social Network Analysis 12 2.3.2 Item Recommendation 16 2.3.3 Emotion Analysis and Recommendation using emotions 20 Chapter 3 User Behavior in Social Cataloging Services 24 3.1 Motivation 24 3.2 Datasets 27 3.2.1 LibraryThing 27 3.2.2 Userstory Book 28 3.2.3 Flixster 30 3.2.4 Preliminary Analysis 31 3.3 Characteristics of Users in Social Cataloging Services 36 3.3.1 Assortativity 36 3.3.2 Reciprocity 37 3.3.3 Homophily 39 3.4 Isolated Users in Social Cataloging Service 41 3.5 Summary 48 Chapter 4 Tag Emotion Based Item Recommendation 51 4.1 Motivation 52 4.2 Weighting of Tags 55 4.2.1 Rating Based Tag Weight 55 4.2.2 Emotion Based Tag Weight 57 4.2.3 Overall Tag Weight 58 4.3 Tensor Factorization 59 4.3.1 High Order Singular Value Decomposition 60 4.4 A Running Example 62 4.5 Experimental Evaluation 66 4.5.1 Dataset 66 4.5.2 Experimental Results 68 4.6 Summary 76 Chapter 5 Improving Item Recommendation using Probabilistic Ranking 78 5.1 Motivation 78 5.2 Generating the additional data 79 5.3 BM25 based candidate ranking 81 5.4 Experimental Evaluation 84 5.4.1 Data addition 84 5.4.2 Recommendation Performances 87 5.5 Case Study 96 5.6 Summary 99 Chapter 6 Conclusions 100 Bibliography 103 초록 117Docto

Cross domain recommender systems using matrix and tensor factorization

Today, the amount and importance of available data on the internet are growing exponentially. These digital data has become a primary source of information and the people’s life bonded to them tightly. The data comes in diverse shapes and from various resources and users utilize them in almost all their personal or social activities. However, selecting a desirable option from the huge list of available options can be really frustrating and time-consuming. Recommender systems aim to ease this process by finding the proper items which are more likely to be interested by users. Undoubtedly, there is not even one social media or online service which can continue its’ work properly without using recommender systems. On the other hand, almost all available recommendation techniques suffer from some common issues: the data sparsity, the cold-start, and the new-user problems. This thesis tackles the mentioned problems using different methods. While, most of the recommender methods rely on using single domain information, in this thesis, the main focus is on using multi-domain information to create cross-domain recommender systems. A cross-domain recommender system is not only able to handle the cold-start and new-user situations much better, but it also helps to incorporate different features exposed in diverse domains together and capture a better understanding of the users’ preferences which means producing more accurate recommendations. In this thesis, a pre-clustering stage is proposed to reduce the data sparsity as well. Various cross-domain knowledge-based recommender systems are suggested to recommend items in two popular social media, the Twitter and LinkedIn, by using different information available in both domains. The state of art techniques in this field, namely matrix factorization and tensor decomposition, are implemented to develop cross-domain recommender systems. The presented recommender systems based on the coupled nonnegative matrix factorization and PARAFAC-style tensor decomposition are evaluated using real-world datasets and it is shown that they superior to the baseline matrix factorization collaborative filtering. In addition, network analysis is performed on the extracted data from Twitter and LinkedIn

Automated Recommender Systems

Recommender systems have been existing accompanying by web development, driving personalized experience for billions of users. They play a vital role in the information retrieval process, overcome the information overload by facilitating the communication between business people and the public, and boost the business world. Powered by the advances of machine learning techniques, modern recommender systems enable tremendous automation on the data preprocessing, information distillations, and contextual inferences. It allows us to mine patterns and relationships from massive datasets and various data resources to make inferences. Moreover, the fast evolvement of deep learning techniques brings vast vitality and improvements dived in both academic research and industry applications. Despite the prominence achieved in the recent recommender systems, the automation they have been achieved is still limited in a narrow scope. On the one hand, beyond the static setting, real-world recommendation tasks are often imbued with high-velocity streaming data. On the other hand, with the increasing complexity of model structure and system architecture, the handcrafted design and tuning process is becoming increasingly complicated and time-consuming. With these challenges in mind, this dissertation aims to enable advanced automation in recommender systems. In particular, we discuss how to update factorization-based recommendation models adaptively and how to automatically design and tune recommendation models with automated machine learning techniques. Four main contributions are made via tackling the challenges: (1) The first contribution of this research dissertation is the development of a tensor-based algorithm for streaming recommendation tasks. (2) As deep learning techniques have shown their superiority in recommendation tasks and become dominant in both academia and industry applications, the second contribution is exploring and developing advanced deep learning algorithms to tackle the recommendation problem with the streaming dataset. (3) To alleviate the burden of human efforts, we explore adopting automated machine learning in designing and tuning recommender systems. The third contribution of this dissertation is the development of a novel neural architecture search approaches for discovering useful features interactions and designing better models for the click-through rate prediction problem. (4) Considering a large number of recommendation tasks in industrial applications and their similarities, in the last piece of work work, we focus on the hyperparameter tuning problem in the transfer-learning setting and develop a transferable framework for meta-level tuning of machine learning models

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field