Empirical analysis of session-based recommendation algorithms

Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning-based (“neural”) approaches to session-based recommendations have been proposed. However, previous research indicates that today’s complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy. With this work, our goal is to shed light on the state of the art in the area of session-based recommendation and on the progress that is made with neural approaches. For this purpose, we compare twelve algorithmic approaches, among them six recent neural methods, under identical conditions on various datasets. We find that the progress in terms of prediction accuracy that is achieved with neural methods is still limited. In most cases, our experiments show that simple heuristic methods based on nearest-neighbors schemes are preferable over conceptually and computationally more complex methods. Observations from a user study furthermore indicate that recommendations based on heuristic methods were also well accepted by the study participants. To support future progress and reproducibility in this area, we publicly share the session-rec evaluation framework that was used in our research

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

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques

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