A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Integrating Case-Based Reasoning with Adaptive Process Management

The need for more flexiblity of process-aware information systems (PAIS) has been discussed for several years and different approaches for adaptive process management have emerged. Only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. The knowledge about changes has not yet been exploited by any of these systems. To overcome this practical limitation, PAIS must capture the whole process life cycle and all kinds of changes in an integrated way. They must allow users to deviate from the predefined process in exceptional situations, and assist them in retrieving and reusing knowledge about previously performed changes. In this report we present a proof-of concept implementation of a learning adaptive PAIS. The prototype combines the ADEPT2 framework for dynamic process changes with concepts and methods provided by case-based reasoning(CBR) technology

지식 기반 대화에서의 대화 특성을 활용한 지식 선택 및 랭킹 방법

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·컴퓨터공학부, 2022. 8. 이상구.Knowledge grounded conversation (KGC) model aims to generate informative responses relevant to both conversation history and external knowledge. One of the most important parts of KGC models is to find the knowledge which provides the basis on which the responses are grounded. If the model selects inappropriate knowledge, it may produce responses that are irrelevant or lack knowledge. In this dissertation, we study the methods of leveraging conversational characteristics to select or rank the knowledge for knowledge grounded conversation. In particular, this dissertation provides novel two methods, where one of which focuses on the sequential structure of multi-turn conversation, and the other focuses on utilizing local context and topic of a long conversation. We first propose two knowledge selection strategies of which one preserves the sequential matching features and the other encodes the sequential nature of the conversation. Second, we propose a novel knowledge ranking model that composes an appropriate range of relevant documents by exploiting both the topic keywords and local context of a conversation. In addition, we apply the knowledge ranking model in quote recommendation with our new quote recommendation framework that provides hard negative samples to the model. Our experimental results show that the KGC models based on our proposed knowledge selection and ranking methods outperform the competitive models in terms of groundness and relevance.지식 기반 대화 모델은 대화 기록과 외부 지식 이 두 가지 모두에 관련된 응답을 생성하는 것을 목표로 한다. 지식 기반 대화 모델의 가장 중요한 부분 중 하나는 응답의 기반을 제공하는 지식을 찾는 것이다. 지식 기반 모델이 주어진 문맥에 부적합한 지식을 찾는 경우 관련성이 떨어지거나 지식이 부족한 응답이 생성될 수 있다. 이 문제를 해결하기 위해 이 논문에서는 지식 기반 대화를 위해 대화 여러 특성을 활용하여 지식을 선정하는 지식 선택 모델과 지식 순위 모델을 제시한다. 구체적으로 본 논문에서는 다중 턴 대화에서의 순차적 구조 또는 응답 이전 문맥과 대화의 주제를 활용하는 새로운 두 가지 방법을 제시한다. 첫 번째 방법으로써 본 논문은 두 가지 지식 선택 전략을 제안한다. 제안하는 전략 중 하나는 지식과 대화 턴 간의 순차적 매칭 특징을 보존하는 방법이고 다른 전략은 대화의 순차적 특성을 인코딩하여 지식을 선택하는 방법이다. 두 번째로 본 논문은 대화의 주제 키워드와 응답 바로 이전의 문맥을 모두 활용하여 적절한 범위의 관련 문서들로 검색 결과를 구성하는 새로운 지식 순위 모델을 제안한다. 마지막으로 지식 순위 모델의 적응성 검증을 위해 정답 인용구와 의미적으로 유사하지만 정답은 아닌 인용구의 집합을 인용구 순위 모델에 제공하는 인용구 추천 프레임워크를 제안한다. 제안된 지식 선택 및 순위 모델을 기반으로 하는 지식 기반 대화 모델이 경쟁 모델보다 외부 지식 및 대화 문맥과의 관련성 측면에서 우수하다는 것을 사람 간의 대화 데이터를 이용한 다수의 실험을 통해 검증하였다.Abstract 1 1. Introduction 17 2. Background and Related Works 25 2.1 Terminology 25 2.2 Overview of Technologies for Conversational Systems 27 2.2.1 Open-domain Dialogue System 27 2.2.2 Task-oriented Dialogue System 29 2.2.3 Question Answering System 29 2.3 Components of Knowledge Grounded Conversation Model 31 2.4 Related Works 36 2.4.1 KGC datasets 36 2.4.2 Soft Selection-based KGC Model 36 2.4.3 Hard Selection-based KGC Model 37 2.4.4 Retrieval-based KGC Models 39 2.4.5 Response Generation with Knowledge Integration 39 2.4.6 Quote Recommendation 42 2.5 Evaluation Methods 44 2.6 Problem Statements 47 3. Knowledge Selection with Sequential Structure of Conversation 48 3.1 Motivation 48 3.2 Reduce-Match Strategy & Match-Reduce Strategy 49 3.2.1 Backbone architecture 51 3.2.2 Reduce-Match Strategy-based Models 52 3.2.3 Match-Reduce Strategy-based Models 56 3.3 Experiments 62 3.3.1 Experimental Setup 62 3.3.2 Experimental Results 70 3.4 Analysis 72 3.4.1 Case Study 72 3.4.2 Impact of Matching Difficulty 75 3.4.3 Impact of Length of Context 77 3.4.4 Impact of Dialogue Act of Message 78 4. Knowledge Ranking with Local Context and Topic Keywords 81 4.1 Motivation 81 4.2 Retrieval-Augmented Knowledge Grounded Conversation Model 85 4.2.1 Base Model 86 4.2.2 Topic-aware Dual Matching for Knowledge Re-ranking 86 4.2.3 Data Weighting Scheme for Retrieval Augmented Generation Models 89 4.3 Experiments 90 4.3.1 Experimental Setup 90 4.3.2 Experimental Results 94 4.4 Analysis 98 4.4.1 Case Study 98 4.4.2 Ablation Study 99 4.4.3 Model Variations 104 4.4.4 Error Analysis 105 5. Application: Quote Recommendation with Knowledge Ranking 110 5.1 Motivation 110 5.2 CAGAR: A Framework for Quote Recommendation 112 5.2.1 Conversation Encoder 114 5.2.2 Quote Encoder 114 5.2.3 Candidate Generator 115 5.2.4 Re-ranker 116 5.2.5 Training and Inference 116 5.3 Experiments 117 5.3.1 Experimental Setup 117 5.3.2 Experimental Results 119 5.4 Analysis 120 5.4.1 Ablation Study 120 5.4.2 Case Study 121 5.4.3 Impact of Length of Context 121 5.4.4 Impact of Training Set Size per Quote 123 6. Conclusion 125 6.1 Contributions and Limitations 126 6.2 Future Works 128 Appendix A. Preliminary Experiments for Quote Recommendations 131 A.1 Methods 131 A.1.1 Matching Granularity Adjustment 131 A.1.2 Random Forest 133 A.1.3 Convolutional Neural Network 133 A.1.4 Recurrent Neural Network 134 A.2 Experiments 135 A.2.1 Baselines and Implementation Details 135 A.2.2 Datasets 136 A.2.3 Results and Discussions 137 초록 162박

Eino: An Intelligent Tutor For The University Of Central Florida Infinity Web Applets

This study investigated the various methods involved in creating an intelligent tutor for the University of Central Florida Infinity Web Applets (UCF Infinity Web Applets). After conducting research into various methods, two major methods emerged and they are: solving the problem for the student and helping the student when they become stymied and unable to solve the problem. A storyboard was created to show the interactions of the student and system along with a list of features that were desired to be included in the tutoring system. From the storyboard and list of features, an architecture was created to handle all of the interactions and features. After the initial architecture was designed, the development of the actual system was started. The architecture underwent a multitude of changes to conclude with a working system, EINO. The final architecture of EINO incorporated a case based reasoning system to perform pattern recognition on the student\u27s input into the UCF Infinity Web Applets. The interface that the student interacts with was created using flash. EINO was implemented in three of the labs from the UCF Infinity Web Applets. A series of tests were performed on the EINO tutoring system to prove that the system could actually perform each and every one of the features listed initially. The final test was a simulation of how the EINO would perform under a set of given cases. Test subjects with the same educational level as the target group were chosen to spend an unlimited time using each of the three labs. Each of the test subjects filled out a survey on every lab to determine if the EINO system produced a helpful output