Explicitly Leveraging Context Information for Deep Sequential Recommendation = 딥러닝 기반의 시퀀셜 추천을 위한 컨텍스트 정보의 명시적 활용 모델 연구

다국어 초록 (Multilingual Abstract)

A recommender system or recommendation model is a system that recognizes the preferences of users and suggests products or content that are likely to be consumed. The recommender systems play a key role in numerous services as they greatly contribute to increasing user engagement and revenue. Recent advancements in machine learning and deep learning technology have significantly improved the performance of recommender systems, and various studies are actively being conducted to improve performance in a wide variety of situations from diverse perspectives.
In this dissertation, I propose novel model structures that improve performance by explicitly leveraging context information in a deep learning-based sequential recommendation model. A deep learning-based sequential recommendation model typically consists of three components: an item encoder, a sequence encoder, and an item decoder. The purpose of this dissertation is to propose new structures for each component to effectively utilize context information and thereby improve the performance of the recommender systems.
Firstly, for the item encoder, I propose a proxy-based model to tackle the lack of learning opportunities for infrequent items by utilizing item attributes and context information. By expressing items as a combination of proxy vectors, item representations are computed with proper quality, significantly improving recommendation accuracy. Secondly, for the sequence encoder, I propose three methods that improve recommendation accuracy in a parameter-efficient manner by utilizing the session information. Methods of injecting session information in the form of tokens, segments, and time-aware self-attention are proposed. Lastly, for the item decoder, I propose a novel layer that improves recommendation accuracy and explainability by separately computing attention scores for item attribute information, context information, and co-occurrence information. The computed attention scores can be used to generate direct recommendation explanation sentences with the help of external systems such as large language models.
To verify the effectiveness of the models presented in this dissertation, experiments were conducted on real-world datasets, and it was confirmed that the best performance was achieved when not only each model but also the three improvements were applied simultaneously. The proposed model is expected to increase the performance of the recommendation system and improve user satisfaction in an environment where context information is provided.

국문 초록 (Abstract)

추천 시스템, 혹은 추천 모델이란, 서비스 사용자의 선호를 파악하여 맞춤형으로 상품이나 컨텐츠를 제안하는 시스템이다. 이러한 추천 시스템은 사용자의 서비스 만족도를 높이고, 서비스 제공자의 수익을 증대시키는 데 크게 기여하기 때문에 많은 서비스에서 핵심적인 역할을 하고 있다. 최근 머신러닝과 딥러닝 기술의 발전으로 추천 시스템의 성능이 크게 향상되었으며, 다양한 상황에서, 여러 관점에서 성능을 높이기 위한 연구들이 활발히 진행되고 있다.
본 논문에서는 사용자의 활동 이력을 시퀀스 데이터로 사용하는 딥러닝 기반의 시퀀셜 추천 모델에서, 컨텍스트 정보를 명시적으로 활용하여 성능을 향상시킬 수 있는 새로운 모델 구조를 제안한다. 딥러닝 기반의 시퀀셜 추천 모델은 통상적으로 아이템 인코더, 시퀀스 인코더, 그리고 아이템 디코더 이렇게 세 가지 요소로 구성되어 있다. 본 논문의 목표는 각 요소에서 컨텍스트 정보를 활용하여 보다 효과적으로 학습할 수 있도록 새로운 구조를 제시하고, 이를 통해 추천 시스템의 성능을 향상시키는 것이다.
첫째로, 아이템의 속성 정보와 컨텍스트 정보를 활용하여 비인기 아이템의 학습 기회 부족 문제를 해결하기 위한 새로운 아이템 인코더 모델을 제안한다. 아이템을 프록시(proxy) 벡터의 조합으로 표현하여, 파라미터 효율적이면서도 보장된 품질의 아이템 벡터를 계산하여 추천 정확도를 크게 향상시킨다. 둘째로, 컨텍스트 정보 중 하나인 세션 정보를 활용하여 파라미터 효율적으로 추천 정확도를 향상시키는 새로운 시퀀스 인코더 모델을 제안한다. 세션 정보를 토큰의 형태, 세그먼트의 형태, 그리고 시간을 고려한 주목 연산의 형태로 주입하는 방식을 제안하여 추천 정확도를 효율적으로 향상시킨다. 마지막으로, 아이템의 속성 정보와 컨텍스트 정보, 그리고 동시 발생(co-occurrence) 정보를 분리하여 주목(attention)시키는 새로운 아이템 디코더 모델을 제안하며 이를 통해 추천 정확도와 설명력을 개선한다. 분리된 주목 정보는 대형 언어 모델 등의 외부 시스템의 도움을 받아서 직접적인 추천 설명 문장을 생성하는 데 사용될 수 있다.
본 논문에서 제시된 모델들의 유효성을 검증하기 위해 현실에서 사용되는 추천 관련 데이터셋에서 실험을 진행하였고, 각각의 모델 뿐만 아니라 세가지 개선점을 동시에 적용했을때 가장 좋은 성능을 보인다는것 까지 확인하였다. 제안한 모델는 컨텍스트 정보가 제공되는 환경에서 추천 시스템의 정확도를 높이고 사용자의 만족도를 향상시킬 것으로 기대한다.

목차 (Table of Contents)

• Abstract i
• Chapter 1 Introduction 1
• 1.1 Recommender Systems 1
• 1.2 Deep Recommendation Models 4
• 1.3 Dissertation Outline 12
• 1.4 Related Publications 13
• Chapter 2 Background 14
• 2.1 Problem Formulation 14
• 2.1.1 General Recommendation Model 14
• 2.1.2 Sequential Recommendation Model 15
• 2.1.3 Attribute and Context-aware Recommendation Model 16
• 2.1.4 Training Strategy 18
• 2.1.5 Evaluation Protocols 19
• 2.1.6 Chapter Diagram 19
• 2.2 Related Works 21
• 2.2.1 Sequential Recommendation 21
• 2.2.2 Session and Time-aware Recommendation 22
• 2.2.3 Parameter-efficient Recommendation 23
• 2.2.4 Attribute and Context-aware Recommendation 24
• 2.2.5 Explainable Recommendation 24
• 2.2.6 Comparison to Baselines 25
• 2.3 Taxonomy of Recommendation Models 26
• 2.3.1 Objective 26
• 2.3.2 Model Architecture 28
• 2.3.3 Available Data 30
• 2.3.4 Explainability 31
• 2.3.5 Miscellaneous 32
• 2.3.6 Scope of Dissertation 32
• Chapter 3 Proxy-based Representation for Infrequent-complementing Item Encoder 34
• 3.1 Introduction 34
• 3.2 Related Work 40
• 3.3 Model 42
• 3.3.1 Problem Definition 42
• 3.3.2 Background 43
• 3.3.3 Proxy-based Item Representation 47
• 3.3.4 Complexity of Parameters 51
• 3.3.5 Properties of PIR 52
• 3.3.6 Training Objective 53
• 3.4 Experiment 54
• 3.4.1 Experimental Settings 56
• 3.4.2 Overall Performance Comparison (RQ1) 61
• 3.4.3 Performance on Infrequent Items (RQ2) 62
• 3.4.4 Parameter Efficiency (RQ3) 63
• 3.4.5 Proxy Analysis (RQ4) 64
• 3.4.6 Ablation Study 67
• 3.4.7 Hyper-parameter Tuning 68
• Chapter 4 Parameter-efficient Session and Time-aware Attention for Sequence Encoder 70
• 4.1 Introduction 70
• 4.2 Approach 76
• 4.2.1 Background 76
• 4.2.2 Session Token 78
• 4.2.3 Session Segment Embedding 78
• 4.2.4 Temporal Self-Attention 79
• 4.3 Experiment 83
• 4.3.1 Experimental Design 83
• 4.3.2 Recommendation Performance 86
• 4.3.3 Impact of Hyper-parameters of SSE and TSA 88
• 4.3.4 Impact of Hyper-parameters of Model 88
• Chapter 5 Aspect-wise Cross-attention for Explainable Item Decoder 90
• 5.1 Introduction 90
• 5.2 Approach 98
• 5.2.1 Problem Definition 98
• 5.2.2 Backbone 99
• 5.2.3 Aspect-wise Cross-attention 102
• 5.2.4 Training Objective 103
• 5.3 Experiment 103
• 5.3.1 Experimental Settings 104
• 5.3.2 Performance Comparison (RQ1) 109
• 5.3.3 Explainability Anaylsis (RQ2) 114
• 5.3.4 Effectiveness of Explanation (RQ3) 117
• 5.3.5 Ablation Study (RQ4) 117
• Chapter 6 Conclusion 121
• 6.1 Summary 121
• 6.2 Final Analysis 123
• 6.3 Limitations and Future Work 125
• Bibliography 127
• 초록 155
• 감사의 글 157

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