QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach

SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning

Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. The previous works have been modeling the session as a variable-length of a sequence of items and learning the representation of both individual items and the aggregated session. Recent research has applied graph neural networks with an attention mechanism to capture complicated item transitions and dependencies by modeling the sessions into graph-structured data. However, they still face fundamental challenges in terms of data and learning methodology such as sparse supervision signals and noisy interactions in sessions, leading to sub-optimal performance. In this paper, we propose SR-GCL, a novel contrastive learning framework for a session-based recommendation. As a crucial component of contrastive learning, we propose two global context enhanced data augmentation methods while maintaining the semantics of the original session. The extensive experiment results on two real-world E-commerce datasets demonstrate the superiority of SR-GCL as compared to other state-of-the-art methods.Comment: 11 pages. This paper has been accepted by DLG-AAAI'2

We Had to Start Somewhere: Applying the Anti-Racist LibGuide Framework Created by Jaime Ding and the Cal Poly Team

A team from the Health Sciences Library (HSL) applied an anti-racist framework, developed by a team from Cal Poly led by Jaime Ding, to a pilot batch of HSL LibGuides. The HSL team adapted Cal Poly’s rubric for assessing LibGuides and developed tiered recommendations, based on effort and time required, that liaison librarians can apply to their LibGuides. In the session, presenters provided an overview of Ding’s approach; shared practical approaches for updating LibGuides using the framework; discussed lessons learned and strategies to address potential resistance to suggested changes; and offered resources to participants, including a rubric and recommendations around how to conduct a “tiered” anti-racism review of LibGuides. The session included a facilitated small-group activity and a group discussion

트랜스포머 기반 음악 스트리밍 세션 추천 시스템

학위논문(석사) -- 서울대학교대학원 : 데이터사이언스대학원 데이터사이언스학과, 2022. 8. 신효필.Recommendation systems have grown in popularity over the last few years, with the rise of big data and development of computing resources. Compared to simple rule based methods or content based filtering methods used for recommendation during the early development stage of recommendation systems, recent methodologies try to implement much more complex models. Latent factor models and collaborative filtering methods were developed to find similarities between users and items without actually knowing their characteristics, and gained popularity. Various item domains, mainly movie and retail, have extensively used these recommendation algorithms. With the development of deep learning architectures, various deep learning based recommendation systems emerged in recent years. While a lot of them were focused on generating the predicted item ratings when given a big data comprised of user ids, item ids, and ratings, there were some efforts to generate next-item recommendations as well. Next-item recommendations receive a session or sequence of actions by some user, and try to predict the next action of a user. NVIDIA recently used Transformers, a deep learning architecture in the field of Natural Language Processing (NLP), to build a session based recommendation system called Transformers4Rec. The system showed state of the art performances for the usual movie and retail domains. In the music domain, unfortunately, advanced models for session-based recommendations have been explored to a small extent. Therefore, this thesis will attempt to apply Transformer based architectures to session-based recommendation for music streaming, by utilizing a dataset from Spotify and framework from NVIDIA. In this thesis, unique characteristics of music data that validates this research’s purpose are explored. The effectiveness of Transformer architectures on music data are shown with next-item prediction performances on actual user streaming session data, and methods for feature engineering and data preprocessing to ensure the best prediction results are investigated. An empirical analysis that compares various Transformer architectures is also provided, with models further analyzed with additional feature information.최근 트랜스포머 기반 추천시스템들이 다양한 분야에서 높은 성능을 보여왔다. 하지만 음악 스트리밍 분야에는 적용되지 않았었고, 이 논문을 통해 음악 스트리밍 분야에 트랜스포머 기반 세션 추천시스템이 어떤 성능을 보여주는지 탐색해 보았다. 데이터 전처리를 통해 유저들이 음악을 실제로 좋아해서 들었을 법한 세션들만 남기려 노력했고, 세션 기반 추천시스템에 맞게 데이터를 정제했다. 음악과 관련된 다양한 정보들도 모델 훈련에 반영하기 위해 카테고리 형태로 바꿔주었고, 훈련 자체는 세션 기반 추천시스템에서 자주 쓰이는 점진적 훈련법을 활용했다. 최종 실험 결과에서는 데이터의 비정제성과 비밀집성을 극복하고 비슷한 데이터셋과 경쟁력을 갖추는 성과를 보여주었다. 이 연구를 통해 음악 스트리밍 세션 추천시스템에 트랜스포머 기반 모델이라는 새로운 가능성을 보여 주었고, 추후 연구자들이 참고할 수 있는 시작점을 제공하였다.1 Introduction 1 1.1 Research Topic 1 1.2 Purpose of Research 2 1.3 Need for Research 3 1.3.1 Recent Trends 3 1.3.2 Dataset Characteristics 4 2 Related Works 9 2.1 Overview of NLP and RecSys 9 2.2 Past Works on Incorporating Features 12 3 Methodology 13 3.1 Music Streaming Sessions Dataset 13 3.2 Music Recommendation Model 14 3.2.1 NVTabular 15 3.2.2 Transformers4Rec 15 3.3 Feature Embeddings 16 3.4 Session Information 17 3.5 Transformer Architectures 18 3.6 Metrics 19 4 Experiments 21 4.1 Data Preprocessing 21 4.2 Embedding 23 4.2.1 No features 23 4.2.2 Session features 23 4.2.3 Song features 25 4.3 Hyperparameters 27 4.4 Training 28 4.4.1 Problem Statement 28 4.4.2 Pipeline 28 4.4.3 Incremental Training, Evaluation 29 4.5 Results 30 4.5.1 Simple item IDs 30 4.5.2 Item IDs + Session Information 31 4.5.3 Item IDs + Session Information + Track Metadata 32 5 Conclusion and Future Works 34 Bibliography 36 초 록 41석

Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502