Trinity: Syncretizing Multi-/Long-tail/Long-term Interests All in One

Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We figure that long-term cues can be the cornerstone since they reveal multi-interest and clarify long-tail interest. Inspired by the observation, we propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Trinity is more appropriate for large-scale industry scenarios because of its modest computational overheads. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios

User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

Recommending on graphs: a comprehensive review from a data perspective

Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.Comment: Accepted by UMUA

Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Personal Web API Recommendation Using Network-based Inference

Abstract. In this paper, we evaluate a generic network-based inference algorithm for Web API recommendation. Based on experimental data collected from the Programmable Web repository, we construct two tripartite networks: one where the nodes are Web APIs, users and mashups, and another where the nodes are Web APIs, users and tags. Experimental results show that the network-based inference algorithm yields higher precision, ranking quality and personalization score when applied to the second network. This approach also outperforms three existing methods: a global ranking method, a collaborative filtering method and the Programmable Web recommendation tool

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

Intelligent techniques for recommender systems

This thesis focuses on the data sparsity issue and the temporal dynamic issue in the context of collaborative filtering, and addresses them with imputation techniques, low-rank subspace techniques and optimizations techniques from the machine learning perspective. A comprehensive survey on the development of collaborative filtering techniques is also included