EMPLOYING LARGE LANGUAGE MODELSFOR DIALOGUE-BASED PERSONALIZEDNEEDS EXTRACTION IN SMART SERVICES
Takuya Nakata1, Sinan Chen2, Sachio Saiki3 and Masahide Nakamura2
1

Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho,Nada-ku, Kobe, Japan
2
Center of Mathematical and Data Sciences, Kobe University, 1-1
Rokkodai-cho, Nada-ku, Kobe, Japan3
School of Data and Innovation, Kochi University of Technology, 185
Miyanokuchi, Tosayamada, Kami, Japan

ABSTRACT
Research concerning the personalization of services encompasses approaches such asmachine learning and dialogue agents; however, the explainability of the recommendation
process remains a challenge. Previous studies have proposed dialogue-based needs
extraction systems utilizing the 6W1H need model, but extracting complex needs using
simple natural language processing proved challenging. In this research, we embark on thedevelopment of an Application Programming Interface (API) that extracts user needs fromnatural language by leveraging the rapidly advancing Large Language Models (LLM), andon constructing a dialogue-based needs extraction system using this API. For evaluation,
we conducted a verification on 100 needs with the aim of assessing the accuracy andcomprehensiveness of the outputs from the needs extraction and restoration API. Throughthis study, it became feasible to extract needs with high accuracy and comprehensivenessfrom complex natural language using LLM.

KEYWORDS
Personalization, Need, Large Language Model, Natural Language Processing, Dialogue
Agent

1. INTRODUCTION
With the widespread adoption of smart services in society, research on service personalization,
which tailors service delivery and functionality to users' preferences, thereby creating added
value, has become increasingly prevalent [1,2]. Two primary approaches exist in the study ofindividual adaptation: recommendations through machine learning and function
recommendations and updates through dialogue [3]. Research has also been conducted onconversational recommendation systems, which integrate these approaches [4]. However, achallenge arises in explaining to users the rationale behind the recommendations [5,6].
Traditional research proposed the 6W1H need model, which expresses the user's need regarding
how they wish to execute a service through seven elements: where, when, who, whom, why,
what, and how [7]. This model aimed to address the challenge of explaining therecommendation process. In this framework, morphological analysis and syntactic analysis wereDavid C. Wyld et al. (Eds): ICCSEA, NLAI, SCAI, CSIA, IBCOM, SEMIT, NECO, SPPR, MLDS -2023
pp. 21-33, 2023. CS & IT - CSCP 2023
DOI: 10.5121/csit.2023.132403

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used to extract 6W1H elements from user needs utterances. However, there remained issues inextracting complex needs and specific elements.
The aim of this research is to address the challenges identified in previous studies and to enhancethe diversity and accuracy of needs extraction. A key idea revolves around utilizing theGenerative Pre-trained Transformer (GPT) model of Large Language Models (LLM), which has
seen an explosive increase in usage in recent years, for the construction of a needs extraction
Application Programming Interface (API) and the reconstruction of the dialogue-based needs
extraction system [8]. The approach of this research is as follows:(A1) Construction of the needs extraction and restoration API using LLM.
(A2) Design of the overall architecture.
(A3) Design of the dialogue flow.
For evaluation, the proposed Needs Extraction API and Need Restoration API will be tested with
100 inputs to assess the accuracy and comprehensiveness of the outputs, and whether the 6W1H
elements can be sufficiently extracted.

2. PRELIMINARIES
2.1. Personalization
In the digital domain, personalization refers to the process of modifying a system's functionality,interface, and content to enhance its relevance to an individual or a group of individuals. There
are two primary approaches to achieve personalization: machine learning and dialogue. The
machine learning approach involves deep learning based on a user's past service usage history,
aiming to predict and recommend services the user may prefer [9,10]. However, this approach
faces challenges in explainability since the recommendation process often operates as a blackbox. The dialogue-based approach updates the user model, which represents user preferences,through interactions between the user and a virtual agent [11]. Additionally, this approachprovides service updates. Dialogue-based personalization has been implemented in devices suchas smart speakers and healthcare agents [12]. In recent years, conversational recommendation
systems have emerged as an approach that integrates both machine learning and dialogue. Thesesystems allow for user inquiries and feedback through dialogue and recommend service
functionalities based on dialogue logs, using machine learning.

2.2. Conventional Research: Interactive Needs Extraction System
In conversation-based recommendation systems, the explainability of the recommendation
process, similar to personalized adaptation through machine learning, is a challenge. Previous
research aimed to introduce an interpretable need model, situated between dialogue and machinelearning, to make the system’s interpreted needs explainable to users. Specifically, they proposeda personal adaptation system as shown in Figure 1, with a particular focus on constructing a userneeds extraction method. This proposed system extracts user needs through a dialogue-based userneeds extraction method, which is then employed in machine learning to recommend services. In
the extraction method from previous research, conversational content was gathered through voice
dialogue agents, and needs data represented in a 6W1H format, which users could easily
comprehend, were extracted and stored using natural language processing. The 6W1H needmodel consists of the following seven elements that depict how users want to utilize a service:- where: The location where the service is performed.
- when: The time when the service is performed.

Computer Science & Information Technology (CS & IT)23- who: The main entity performing the service.
- whom: The target of the service.

Figure 1. Overall architecture of the dialogue-based needs extraction system- why: The reason or circumstances for performing the service.
- what: The specific action performed in the service.
- how: The method or tool used to execute the service.
This model employs the 6W1H analytical approach, a common framework for language usage,
communication, and information organization, to address user needs effectively. Need data
represented in the 6W1H format are referred to as 6W1H need data. For instance, the need “Iwant the smart speaker to notify my grandfather in the living room with a loud sound at 6:30
every morning if the weather forecast predicts rain” can be represented in 6W1H need data as:- where: Living room.
- when: At 6:30 every morning.
- who: None.
- whom: My grandfather.
- why: If the weather forecast predicts rain.
- what: Notify with a loud sound.
- how: Smart speaker.
Traditional research used morphological and syntactical analysis to guess each element from themeanings of nouns and particles and to extract 6W1H need data. If some 6W1H elements were
missing, the system would repeatedly ask the user questions like, “Where is the location?” toextract as detailed needs as possible from the user during the conversation. Finally, after
extracting the needs, the system conﬁrms with the user to ensure the extracted result matches theuser’s intended speciﬁcs.

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2.3. Challenges in Previous Research
The needs extraction method using morphological and syntactical analysis in previous research
has its limitations. Firstly, distinguishing between who and whom is challenging. Both elements
represent people related to the execution of the service. However, determining whether one is theexecutor or the target of the service is diﬃcult. Moreover, extracting the why element is
problematic. It is challenging to discern the reasons or conditions for wanting to execute a service
based solely on grammar. Additionally, extracting needs from complex sentences is demanding.
It is not possible to accurately extract the 6W1H elements from utterances concerning multiple
service executions. For instance, from the need expression, “I want to watch movies in the livingroom with a video service and do karaoke every morning with a music service,” it is unclear,based solely on dependency relations, whether the where element pertaining to the living room isrelated to the video or music service.

2.4. Large Language Model
The LLM is a natural language processing model trained on extensive text data. It can be
specialized for speciﬁc natural language processing tasks through ﬁne-tuning [13]. GPT,developed by OpenAI, is one such LLM [14]. It is capable of various natural language processing
tasks such as text generation, translation, and document summarization. In 2022, the Artiﬁcial
Intelligence (AI) chat service ChatGPT was introduced, garnering signiﬁcant global attention.
ChatGPT is a service developed based on GPT-3.5 and GPT-4. With GPT, various processes canbe executed by creatively designing natural language processing instructions using prompt
engineering. The prominent method, Few-shot Prompting, enhances answer accuracy byproviding several question and answer pairs as examples [15].

3. PROPOSED METHOD
3.1. Goal & Key Idea
The objective of this study is to enhance the diversity and accuracy of need extraction by
extracting 6W1H need data from complex natural language expressions of needs communicatedthrough interactions with virtual agents. The key idea revolves around the construction of amethod that utilizes the LLM to extract 6W1H need data from intricate natural language
expressions. The approach of this study is as follows:(A1) Construction of the needs extraction and restoration API using LLM.
(A2) Design of the overall architecture.
(A3) Design of the dialogue ﬂow.

3.2. (A1) Construction of the needs extraction and restoration API using LLM
We developed three distinct APIs utilizing the LLM to achieve the conversion between natural
language and 6W1H need data: the Needs Extraction API, the Needs Re-extraction API, and theNeed Restoration API. An API is a mechanism that allows programs to be invoked from outside
the software. The implementation of these three types of APIs enables the extraction, reextraction, and restoration of needs from various programs. The Needs Extraction API isdesigned to transform user needs expressed in natural language into 6W1H need data. The Needs
Re-extraction API supplements missing 6W1H elements from previously extracted 6W1H needdata using new user need utterances. The Need Restoration API reverts 6W1H need data backinto a natural language expression of the need. For the API implementation, OpenAI’s Chat API

Computer Science & Information Technology (CS & IT)25

was employed. The model used is gpt-3.5-turbo, with a sampling temperature of 0.0. Few-shot
Prompting was utilized for the prompt, providing several input-output examples. The
programming language used for the API’s development is Python.

Figure 2. Example of Needs Extraction API output

Figure 3. Example of Need Restoration API input

Two values are input into the Needs Extraction API. The ﬁrst is the user’s need statement innatural language directed towards a virtual agent, specifying a particular service. The second is alist of service candidates for the how (desired service), derived from preliminary string searcheson the need statement. The output yields the 6W1H need data extracted from the need statement
in a JavaScript Object Notation (JSON) array format. For instance, when inputting the needstatement “I want to connect with people who match my interests on SNS and share real-time
content” and providing “SNS” as the how candidate, a result similar to Figure 2 can be expected.
The output is not a single JSON format, but a JSON array, which can extract multiple 6W1H
JSON elements corresponding to multiple needs related to a service or diﬀerent services from asingle utterance.
The Needs Re-extraction API requires three input values. The ﬁrst two, the need statement andthe how candidate list, are the same as the Needs Extraction API. The third is a previously
extracted single 6W1H need data. The output merges the content of the need statement and theprevious 6W1H need data, producing a new 6W1H need element in a JSON array format. If, forinstance, the provided 6W1H need data lacks where and when elements, and the accompanying
statement is “I want to use it every day in the living room”, the output might include where as“living room” and when as “every day”. However, when multiple 6W1H need data are output, itis necessary to determine if each 6W1H need data updates the input need data or is entirely new.

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One potential method for this determination is considering the JSON as a string and identifying
the updated need by the shortest Levenshtein distance.
For the Need Restoration API, the only input is the 6W1H need data. The output is a natural
language sentence representing the need. For instance, when the 6W1H need data shown in
Figure 3 is input, the output might be “Every day, I want to use SNS to connect with people who
match my interests and to share real-time content anywhere.”

3.3. (A2) Design of the overall architecture
The overall architecture of the interactive user needs extraction system utilizing LLM is
illustrated in Figure 4. The frontend application consists of a virtual dialogue agent and theGoogle Speech API [16]. The backend application is composed by integrating various systems
and APIs, with the interactive needs extraction system at its core. These include the user
management system, service management system, Needs Extraction API, Need Restoration API,
and Chat API. The 6W1H need data extracted by the system is stored in the needs databasethrough the interactive needs extraction system.
The system’s operations are executed in the order indicated by the arrows in Figure 4. Initially, auser vocalizes their service needs. This voice is recorded by the dialogue agent. The recorded
voice is then converted into a text-based needs statement through the Google Speech API.
Subsequently, this statement is passed to the interactive needs extraction system. The interactive
needs extraction system ﬁrst queries the user management system to determine whether the user
who expressed the need is registered in the system. The text of the expressed need is normalized
by converting full-width characters into half-width characters and lowercase letters into
uppercase. In case of Japanese, kanji and hiragana characters into katakana are normalized tokatakana characters. By comparing the normalized string with the list of registered service names
retrieved from the service management system, candidates for the how (service name) that theneed targets are extracted. The original expressed need and the how candidates are then fed into
the Needs Extraction API or the Needs Re-extraction API, producing the 6W1H needs data as
output. The needs extraction (or re-extraction) API internally invokes the Chat API using the
GPT model. The extracted 6W1H needs data is accumulated in the MySQL database termed theneeds database.
The process for generating a response is intricate, branching according to the content of theexpressed need and the results of the needs extraction. To summarize, when a need statement isrequired for purposes like conﬁrming a need with the user, the 6W1H need data is provided as
input to the Need Restoration API, which outputs the need statement. Following that, conditional
branching occurs based on the content of the 6W1H need data and the need statement,
formulating the response. This generated response is handed over to the dialogue agent.
Ultimately, the dialogue agent articulates the response to the user.

3.4. (A3) Design of the dialogue ﬂow
There are two types of dialogue ﬂows: (D1) a response to a new needs statement and (D2) a
response to an additional statement made during needs extraction. A single cycle refers to therepetition of dialogue needed to extract one need related to a service. The (D1) ﬂow is applied tothe initial user statement of the cycle, whereas the (D2) ﬂow is applied o user statements from thesecond instance onward. The operational steps for the (D1) response ﬂow to a new needs
statement are as follows:
Step 1. Extract the 6W1H need data from the received statement using the Needs Extraction API.

Computer Science & Information Technology (CS & IT)

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Step 2. If any 6W1H elements are missing, request additional information from the user.
Step 3. If no 6W1H elements are missing, restore the need statement using the Need Restoration
API and conﬁrm with the user if the need has been correctly extracted.
In the (D2) response ﬂow to an additional statement during needs extraction, the same 6W1H
need data can be continuously updated using a common dialogue ID to search the needs database.
The operational steps for this response ﬂow are as follows:
Step 1. Retrieve previously extracted 6W1H need data from the database based on the dialogue
ID.
Step 2. Re-extract new 6W1H elements from the received additional statement and previous
6W1H need data using the Needs Re-extraction API.

Figure 4. Overall architecture and operational ﬂow of the needs extraction system

Step 3. If multiple 6W1H need data are re-extracted, determine the 6W1H need data closest to theprevious 6W1H need data in terms of the Levenshtein distance through calculation. Consider thisdetermined 6W1H need data as the updated data for the current dialogue.

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Step 4. Generate the response statement in the same manner as Steps 2 and 3 of the (D1) ﬂow.

Figure 5. Implementation architecture of needs extraction system

4. IMPLEMENTATION
The implementation architecture is depicted in Figure 5. The Dialogue Agent is implemented
using HyperText Markup Language (HTML) and JavaScript, functioning within a browser. The
Dialogue-based Needs Extraction System is developed in Kotlin, with the Needs databaserealized using MySQL. The User Management System is implemented in Java, and rather thanusing MySQL, MongoDB is employed for the User database to represent complex attributes foreach user in JSON. This facilitates the cross-utilization of the database across multiple systems,
not limited to the proposed system. However, it is also feasible to implement the database using
MySQL if user management is conﬁned within the proposed system. The Service Management
System is developed in Kotlin, with the Service database realized using MySQL. The Needs
Extraction, Re-extraction, and Restoration API have a simple conﬁguration, consisting solely ofsending commands to the GPT Chat API, and are thus implemented on the same Python server.
In this implementation, ﬁve servers are utilized to clarify roles. This allows for ﬂexible
conﬁgurations, such as installing servers handling personal information on-premises while
placing others in the cloud. Alternatively, given that complex computations are performed using
external services via the API, it is possible to virtually realize all ﬁve servers on a single server.

5. NEEDS EXTRACTION & RESTORATION API EVALUATION EXPERIMENT
We conducted an evaluation experiment with the objective of addressing the following researchquestions concerning the needs extraction and restoration API of (A1):
RQ1 Do the Needs Extraction API and Need Restoration API accurately and comprehensively
convert needs?
RQ2 Does the Needs Extraction API suﬃciently extract the 6W1H elements?

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Figure 6. Characteristics of extraction results from Needs Extraction API

5.1. Experimental Setup
In the evaluation experiment for the Needs Extraction API, we generated test data for input to the
API using ChatGPT. We created 100 variations of need statements, assuming various
perspectives such as students, elderly, and working professionals using ChatGPT. These needstatements encompassed a total of 28 diﬀerent services. To examine the basic quality of the API,each need statement focused on only one service, and we refrained from using need statementsthat targeted multiple services. The evaluation involved authors analyzing the output results of
the API, examining its characteristics and conditions. For the evaluation experiment of the Need
Restoration API, we generated 6W1H need data as test input for the API using ChatGPT. The
100 variations of 6W1H need data were crafted by ChatGPT, taking into consideration various
perspectives like students, elderly, and working professionals. The how element of the 6W1H
need data included a total of 21 diﬀerent services. The evaluation process consisted of the authors
analysing the output results of the API to determine its characteristics.

5.2. Experimental Result
The results of the evaluation experiment for the Needs Extraction API are shown in Figures 6 and7. The outcomes presented in Figure 6 are analyses related to three characteristics. Content
accuracy indicates whether the extracted results contain any false information. Content
comprehensiveness signiﬁes whether all crucial information from the spoken sentence has been
extracted. Classiﬁcation accuracy represents whether each extracted need element has been
appropriately categorized into one of the 6W1H elements. For instance, if an element, whichshould be extracted under where, such as “living room”, is categorized under when, then the
output result was evaluated as not meeting classiﬁcation accuracy. The graph also illustrates
output results that simultaneously satisfy all characteristics.
The results in Figure 7 provide an analysis of how many types of the 6W1H elements could be
extracted. In the section on detailed conditions, we analyzed whether at least one of the where,
when, who, or whom elements, which express detailed conditions for service execution, has been
extracted. In the section on overall conditions, we assessed whether the why element, whichconveys the general conditions for service execution in a sentence, was extracted. The graph also
displays the output results that extracted both detailed and general conditions, and those thatextracted either one of the two.

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Figure 7. Conditions extracted by the Needs Extraction API

Figure 8. Characteristics of restoration results from Need Restoration API

The results of the evaluation experiment for the Need Restoration API are displayed in Figure 8.
Content accuracy indicates whether the restored results contain any false information. Content
comprehensiveness signiﬁes whether all essential information from the 6W1H elements has been
covered. The graph also illustrates output results that simultaneously satisfy all characteristics.

5.3. Experimental Discussion
We will discuss the results of the experiment with respect to RQ1 and RQ2. First, considering
RQ1, we analyze whether the conversion of needs was carried out accurately andcomprehensively. In the Needs Extraction API, the content accuracy was 100%, with theextraction results being entirely truthful and accurate. The content comprehensiveness stood at
96%. However, the omitted content was non-core, supplemental information such as “want toshop conveniently” or “wish to have a fun time”. The classiﬁcation accuracy was 89%. There
were many instances where content that should be categorized under detailed conditions likewhen or under what was mistakenly categorized under why. In the Need Restoration API, thecontent accuracy was 94%, and there were instances where the content of why and what was
reversed in the formulation of sentences. The content comprehensiveness was 98%, and when
multiple elements had similar content, some information to be formulated into sentences was
missing. Furthermore, 85% in the extraction API and 92% in the restoration API satisﬁed all theComputer Science & Information Technology (CS & IT)

31

characteristics. From these results, it can be said that in use cases where users verify the correct
extraction of their needs through an interactive needs extraction system, the needs extraction andrestoration API possess high accuracy and comprehensiveness.
Next, concerning RQ2, we consider the suﬃciency of the 6W1H element extraction. In the Needs
Extraction API, 40% could extract detailed conditions, and 40% could extract general conditions.
Moreover, 67% could extract either the detailed conditions or the general conditions. From this, it
is challenging to claim that suﬃcient 6W1H elements are extracted in a single extraction.
However, the types of 6W1H elements that can be extracted from the need sentence depend on
the content of the needs sentence. Furthermore, in use cases where the user is asked about themissing 6W1H conditions during repeated interactions, it can be said that the Needs Extraction
API possesses the capability to extract the 6W1H elements adequately.

6. DISCUSSION
By utilizing the LLM for the mutual conversion between needs statements and 6W1H need data,
we succeeded in extracting needs from complex natural language utterances. Specifically, we cannow extract multiple needs from a single needs statement and have become able to extract the
who and why elements, which were challenging in previous research. From the results of the API
evaluation experiment, it was determined that needs can be accurately extracted with a single
extraction. However, the characteristics regarding the classiﬁcation and diversity of each element
remain uncertain, highlighting the importance of dialogues that inquire about missing 6W1H
elements. To realize a needs extraction dialogue, we leveraged a user search system, service
search system, and needs extraction and restoration API, implementing the needs extraction
system as a distributed microservices architecture. As future challenges, we believe it is essential
to undertake performance evaluation experiments of the entire system and evaluate the system
quality when used by actual users.

7. CONCLUSION
The objective of this research is to construct a method to extract 6W1H need data from complex
natural language needs statements through dialogue with virtual agents, thereby enhancing thediversity and accuracy of needs extraction. As a key idea, we have developed a method to extract
6W1H need data from complex natural language needs statements using the LLM. Speciﬁcally,
we designed and developed three mutual conversion APIs for extracting, re-extracting, andrestoring 6W1H need data from natural language using the LLM. Moreover, we designed theoverall architecture of the dialogue-based needs extraction system using the proposed APIs. By
incorporating the LLM into the API, we have streamlined and redesigned the complex dialogue
ﬂow.
Additionally, we conducted evaluation experiments by inputting 100 test data into the Needs
Extraction API and the Need Restoration API. As a result, the extraction API yielded outputs thatsatisﬁed 85% for accuracy and comprehensiveness, while the restoration API yielded 92%.
Through this research, we have enabled the extraction of 6W1H need data from complex natural
language with high accuracy and comprehensiveness using the LLM. For future research, we areconsidering evaluating the dialogue-based needs extraction system in more practical applications.

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ACKNOWLEDGEMENTS
This research was partially supported by JSPS KAKENHI Grant Numbers JP19H01138,
JP20H05706, JP20H04014, JP20K11059, JP22H03699, JP19K02973, and Young Scientists(No.23K17006)

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[1]

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AUTHORS
Takuya Nakata received Master of System Informatics from Kobe University, and he
did Bachelor degree in Engineering. Currently, he is pursuing his PhD in Engineeringfrom Kobe University. His research interests include Service computing, Software
engineering.

Sinan Chen received a Master of system informatics and a Ph.D. in computational science from Kobe
University, Japan, in 2019 and 2021, respectively. He worked at Kobe University as a research fellow (PD)from the Japan Society for the Promotion of Science (JSPS) in the ﬁrst half of 2022. He is currently an
assistant professor in the Center of Mathematical and Data Science Center at Kobe University, Japan. His
research interests include the smart home, cloud computing, machine learning, and software engineering.
He is a member of the IEEE, ACM, Sigma Xi, CAAI, CCF, IEICE, and IEEJ.
Masahide Nakamura received the B.E., M.E., and Ph.D. degrees in Information and Computer Sciencesfrom Osaka University, Japan, in 1994, 1996, 1999, respectively. From 1999 to 2000, he has been a postdoctoral fellow in SITE at University of Ottawa, Canada. He joined Cybermedia Center at Osaka
University from 2000 to 2002. From 2002 to 2007, he worked for the Graduate School of Information
Science at Nara Institute of Science and Technology, Japan. From 2007 to 2022, he worked for the
Graduate School of System Informatics at Kobe University. He is currently a full professor in the Center ofMathematical and Data Science Center at Kobe University. In 2015, he worked for Universite Grenoble
Alpes as a visiting professor. In 2018, he joined RIKEN Center for Advanced Intelligence Project as avisiting researcher. His research interests include the service/cloud computing, smart home, smart city, andgerontechnology. He is a member of the IEEE, ACM, IEICE and IPSJ.

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Attribution (CC BY) license.