KuaiSim: A Comprehensive Simulator for Recommender Systems

Reinforcement Learning (RL)-based recommender systems (RSs) have garnered considerable attention due to their ability to learn optimal recommendation policies and maximize long-term user rewards. However, deploying RL models directly in online environments and generating authentic data through A/B tests can pose challenges and require substantial resources. Simulators offer an alternative approach by providing training and evaluation environments for RS models, reducing reliance on real-world data. Existing simulators have shown promising results but also have limitations such as simplified user feedback, lacking consistency with real-world data, the challenge of simulator evaluation, and difficulties in migration and expansion across RSs. To address these challenges, we propose KuaiSim, a comprehensive user environment that provides user feedback with multi-behavior and cross-session responses. The resulting simulator can support three levels of recommendation problems: the request level list-wise recommendation task, the whole-session level sequential recommendation task, and the cross-session level retention optimization task. For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research. We also restructure existing competitive simulators on the KuaiRand Dataset and compare them against KuaiSim to future assess their performance and behavioral differences. Furthermore, to showcase KuaiSim's flexibility in accommodating different datasets, we demonstrate its versatility and robustness when deploying it on the ML-1m dataset

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

Use of implicit graph for recommending relevant videos: a simulated evaluation

In this paper, we propose a model for exploiting community based usage information for video retrieval. Implicit usage information from a pool of past users could be a valuable source to address the difficulties caused due to the semantic gap problem. We propose a graph-based implicit feedback model in which all the usage information can be represented. A number of recommendation algorithms were suggested and experimented. A simulated user evaluation is conducted on the TREC VID collection and the results are presented. Analyzing the results we found some common characteristics on the best performing algorithms, which could indicate the best way of exploiting this type of usage information

News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs

Multi-modal Embedding Fusion-based Recommender

Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure