Tag-Aware Recommender Systems: A State-of-the-Art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithm

A Random Walk Model for Item Recommendation in Social Tagging Systems

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users\u27 preferences on an item similarity graph and spreading items\u27 influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation

Exploring Characteristics of Social Classification

Three empirical studies on characteristics of social classification are reported in this paper. The first study compared social tags with controlled vocabularies and title-based automatic indexing and found little overlaps among the three indexing methods. The second study investigated how well tags could be categorized to improve effectiveness of searching and browsing. The third study explored factors and radios that had the most significant impact on tag convergence. Finding of the three studies will help to identify characteristics of those tagging terms that are content-rich and that can be used to increase effectiveness of tagging, searching and browsing

ソーシャルデータの解析に基づく個々人のインフルエンサ推定およびブックマーク予測に関する研究

人々の関心を変化させる手段を確立する事は、商品購入の促進、学習意欲の向上、依存症の緩和治療など、様々なタスクを支援する重要な課題である。筆者はこれまで、ユーザにとって有用と推測される他者のSNSの投稿を提示（根拠と）しながら、それに関連するコンテンツの推薦を行うといった新しい関心誘発のフレームワークを将来ビジョンとして提唱してきた。本論文は、そのビジョンの基盤となる2つのフェーズに関して議論を行っている。1つ目のフェーズでは、想定システムのプロトタイプを実装し、予備実験としてその効果を調査している。具体的には、SNSから抽出された「推薦の根拠」を付随した情報推薦の仕組みによって、その推薦の説得性や受容性、また対象への関心が有意に向上する事を明らかにし、将来ビジョンの社会的意義を改めて主張している。2つ目のフェーズでは、想定システムの社会実装に向けた要素技術の研究開発を行っている。特に、個々人が任意の投稿に対してどの程度関心を持つのかを定量評価し、各ユーザが興味を持つであろう投稿を推測するという技術は、想定システムにおける重要な基礎技術の1つとなる。しかしながら、既存のソーシャルコンピューティングの手法では、その推測方式について未確立のままであった。本論文では、その方式を確立していくために、ユーザ個々人のソーシャルデータを対象とした新しい解析手法を提案し、その有用性を示している。以下にその概要を述べる。まず、他者への反応や関心が示される個々人のソーシャルデータを解析する事で、各ユーザのSNS上でのインフルエンサを推定する手法を提案している。また、全てのユーザに共通した社会的なインフルエンサだけでなく、ユーザごとに異なった個人的なインフルエンサまで推定できるといった先行研究との違いも生み出している。次に、ある投稿とある個人との間の様々な関係性を定式化し、機械学習を行う事によって、各ユーザが任意の投稿に対してブックマークを行うか否かを予め予測可能な事を実証している。加えて、社会ネットワーク上のユーザ群に対してクラスタ分析を行う事で、ユーザタイプに依存したブックマークへの影響因子の違いも明らかにしている。以上、本論文はSNSを組み合わせたより高度な情報推薦の仕組みを確立するために、個々人のソーシャルデータを解析し、SNS上でのインフルエンサを推定する技術や、ブックマークするであろう投稿を予測する技術に関して研究を行ったものである。Discovering a method for changing people’s interests is an important task. For examples, it helps for us to promote various items, to improve students’ motivation to learn, and to care dependent patients. I have advocated a new framework for inducing a user\u27s interests for various things, which recommends various contents to the user while showing several posts that have useful information for the user. This paper discusses two phases that are the bases of this framework. The first phase conducts a preliminary experiment for discussing an expected effect of the assumed framework by using its prototype. Specifically, it shows that a recommender system that advertises items while showing their reasons extracted from SNS, has the effects on the improvement of a user’s receptivity for a new thing and the induction of his/her interest for it. This result helps for me to insist that the assumed framework has the social significances. The second phase develops several component technologies of the assumed framework. Especially, it is one of the important technologies of the assumed framework, to estimate several posts that each user would be interested in (e.g., a post written by a person whom the user likes). However, existing methods in Social Computing cannot realize it. This paper aims to propose a new method that analyzes individual social data and estimates them. Its details are as follows. 1)This paper proposes a method that estimates the influencers for each user by analyzing the user’s reactions to other persons and the user’s interests for other persons in SNS. In addition, it confirms that the proposed method is superior to those of previous researches, because the proposed method can estimate not only a common influencer for all users (Social influencer) but also the different influencers for each user (Personalized influencers). 2)This paper proposes a method that formulates several relationships between each user and a post, and forecasts whether each user would bookmark the post or not. In addition, it examines the differences of the factors for the users to bookmark the post depending on the type of the user by a cluster analysis for them. Summarizing the above, this paper is for an advanced recommender system combined with SNS. It proposes the method that estimates the influencers for each user and the method that forecasts the posts that each user would bookmark.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Matchmakers or tastemakers? Platformization of cultural intermediation & social media’s engines for ‘making up taste’

There are long-standing practices and processes that have traditionally mediated between the processes of production and consumption of cultural content. The prominent instances of these are: curating content by identifying and selecting cultural content in order to promote to a particular set of audiences; measuring audience behaviours to construct knowledge about their tastes; and guiding audiences through recommendations from cultural experts. These cultural intermediation processes are currently being transformed, and social media platforms play important roles in this transformation. However, their role is often attributed to the work of users and/or recommendation algorithms. Thus, the processes through which data about users’ taste are aggregated and made ready for algorithmic processing are largely neglected. This study takes this problematic as an important gap in our understanding of social media platforms’ role in the transformation of cultural intermediation. To address this gap, the notion of platformization is used as a theoretical lens to examine the role of users and algorithms as part of social media’s distinct data-based sociotechnical configuration, which is built on the so-called ‘platform-logic’. Based on a set of conceptual ideas and the findings derived through a single case study on a music discovery platform, this thesis developed a framework to explain ‘platformization of cultural intermediation’. This framework outlines how curation, guidance, and measurement processes are ‘plat-formed’ in the course of development and optimisation of a social media platform. This is the main contribution of the thesis. The study also contributes to the literature by developing the concept of social media’s engines for ‘making up taste’. This concept illuminates how social media operate as sociotechnical cultural intermediaries and participates in tastemaking in ways that acquire legitimacy from the long-standing trust in the objectivity of classification, quantification, and measurement processes

Enhancing explainability and scrutability of recommender systems

Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithm’s behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in filtering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the system’s behavior can be modified accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: • We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between users’ profiles and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. • We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the user’s prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for finding the smallest counterfactual explanations. • We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-specific item representations. We evaluate all proposed models and methods with real user studies and demonstrate their benefits at achieving explainability and scrutability in recommender systems.Unsere zunehmende Abhängigkeit von komplexen Algorithmen für maschinelle Empfehlungen erfordert Modelle und Methoden für erklärbare, nachvollziehbare und vertrauenswürdige KI. Zum Verstehen der Beziehungen zwischen Modellein- und ausgaben muss KI erklärbar sein. Möchten wir das Verhalten des Systems hingegen nach unseren Vorstellungen ändern, muss dessen Entscheidungsprozess nachvollziehbar sein. Erklärbarkeit und Nachvollziehbarkeit von KI helfen uns dabei, die Lücke zwischen dem von uns erwarteten und dem tatsächlichen Verhalten der Algorithmen zu schließen und unser Vertrauen in KI-Systeme entsprechend zu stärken. Um ein Übermaß an Informationen zu verhindern, spielen Empfehlungsdienste eine entscheidende Rolle um Inhalte (z.B. Produkten, Nachrichten, Musik und Filmen) zu filtern und deren Benutzern eine personalisierte Erfahrung zu bieten. Infolgedessen erheben immer mehr In- formationskonsumenten Anspruch auf angemessene Erklärungen für deren personalisierte Empfehlungen. Diese Erklärungen sollen den Benutzern helfen zu verstehen, warum ihnen bestimmte Dinge empfohlen wurden und wie sich ihre früheren Eingaben in das System auf die Generierung solcher Empfehlungen auswirken. Außerdem können Erklärungen für den Fall, dass unerwünschte Inhalte empfohlen werden, wertvolle Informationen darüber enthalten, wie das Verhalten des Systems entsprechend geändert werden kann. In dieser Dissertation stellen wir unsere Beiträge zu Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten vor. • Mit FAIRY stellen wir ein benutzerzentriertes Framework vor, mit dem post-hoc Erklärungen für die von Black-Box-Plattformen generierten sozialen Feeds entdeckt und bewertet werden können. Diese Erklärungen zeigen Beziehungen zwischen Benutzerprofilen und deren Feeds auf und werden aus den lokalen Interaktionsgraphen der Benutzer extrahiert. FAIRY verwendet eine LTR-Methode (Learning-to-Rank), um die Erklärungen anhand ihrer Relevanz und ihres Grads unerwarteter Empfehlungen zu bewerten. • Mit der PRINCE-Methode erleichtern wir das anbieterseitige Generieren von Erklärungen für PageRank-basierte Empfehlungsdienste. PRINCE-Erklärungen sind für Benutzer verständlich, da sie Teilmengen früherer Nutzerinteraktionen darstellen, die für die erhaltenen Empfehlungen verantwortlich sind. PRINCE-Erklärungen sind somit kausaler Natur und werden von einem Algorithmus mit polynomieller Laufzeit erzeugt , um präzise Erklärungen zu finden. • Wir präsentieren ein Human-in-the-Loop-Framework, ELIXIR, um die Nachvollziehbarkeit der Empfehlungsmodelle und die Qualität der Empfehlungen zu verbessern. Mit ELIXIR können Empfehlungsdienste Benutzerfeedback zu Empfehlungen und Erklärungen sammeln. Das Feedback wird in das Modell einbezogen, indem benutzerspezifischer Einbettungen von Objekten gelernt werden. Wir evaluieren alle Modelle und Methoden in Benutzerstudien und demonstrieren ihren Nutzen hinsichtlich Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

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