PEARL: A Review-Driven Persona-Knowledge Grounded Conversational Recommendation Dataset (2024)

Abstract: Conversational recommender systems are an emerging area that has garnered increasing interest in the community, especially with the advancements in large language models (LLMs) that enable sophisticated handling of conversational input. Despite the progress, the field still has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with personaand knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that PEARL contains more specific user preferences, show expertise in the target domain, and provides recommendations more relevant to the dialogue context than those in prior datasets. Furthermore, we demonstrate the utility of PEARL by showing that our downstream models outperform baselines in both human and automatic evaluations. We release our dataset1 and code.2

heading: 1 Introduction
text: Recently, conversational recommender system (CRS) has become an emerging research topic, which aims to elicit user preferences and offer personalized recommendations by engaging in interactive conversations. Towards this goal, an increasing emphasis has been placed on constructing highquality dataset (Li et al., 2018; Liu et al., 2020; Zhou et al., 2020b; Hayati et al., 2020). Existing
*Equal contribution †Corresponding author 1https://huggingface.co/datasets/DLI-Lab/
pearl 2https://github.com/kkmjkim/PEARL
conversational recommendation datasets are mainly collected via crowdsourcing, which is to gather interactions between two crowdworkers where one plays a role as a user (i.e., recommendation seeker) and the other pretends as a recommender.
However, existing dialogues have several limitations that impede downstream CRS models from delivering satisfactory user experiences. First, user preferences expressed in the existing CRS datasets are often less specific. An example includes dialogues with statements like, “I like most genres” as shown in Figure 1. Such dialogues lead downstream models to offer recommendations that are generic and less personalized (He et al., 2023; Zhou et al., 2020a). This happens as crowdworkers, playing the role of users, often lack specific preferences during their tasks, unlike most real-world users who seek personalized recommendations. Second, conversational recommendation dialogues often provide suboptimal recommendations and insufficient explanations alongside recommendations, due to the limited knowledge of crowdworkers (Guo
1105
et al., 2023). A specific example is dialogues containing utterances like “Let me see... How about Tropic Thunder?” as shown in Figure 1. Such responses can lead to CRS models generating less accurate and relevant suggestions. Additionally, the absence of explanations alongside recommendations can be a crucial hurdle, preventing users from grasping the items and the rationale behind the recommendations.
In this paper, we present a conversational recommendation dataset named PEARL (Persona and knowledgE Augmented Recommendation diaLogues) that addresses the limitations of existing datasets. We transform reviews into persona and item knowledge and incorporate large language model (LLM)-based simulators augmented with persona and knowledge. Each simulator is designed to enhance the preference specificity and informativeness of the collected data, respectively. The user simulator plays the role of a recommendation seeker and is equipped with a persona, which is a set of sentences describing features that the user likes and dislikes. Each persona can help the simulator express a distinct user preference of a single real-world user as it is constructed based on the item reviews written by the same user. By simulating users with distinct preferences and specific needs, we can generate dialogues with consistent and clear preferences. The recommender simulator is designed to emulate a recommender with domain knowledge. This addresses the lack of proper recommendations and explanations in existing datasets. Specifically, we incorporate item reviews which can not only provide basic information about items but also reveal soft attributes of items that can be only described through experience (e.g., “feel-good movies” or “upbeat music”) which could be crucial information in the users’ decision-making process.
Our collected dataset includes over 57.2k dialogues simulating over 4k users and covering more than 9k items. Our human evaluation results show that our synthesized dataset is preferred by human raters compared to other crowdsourced dialogues. Moreover, our dataset covers a broader spectrum of user needs as we utilize a large set of authentic reviews written by diverse users.
We also conduct extensive experiments assessing the utility of PEARL through human evaluation and automatic evaluation. The results demonstrate that CRS models trained on PEARL show competitive or better performances in recommendation
and response generation tasks compared to models trained on human-annotated datasets. Additionally, human judges consistently favor responses from models trained on PEARL over those from models trained on crowdsourced datasets, across all metrics. Furthermore, our experimental results empirically validate that CRS models trained on PEARL generalize better to unseen dialogues than the models trained on existing datasets.

heading: 2 Related Work

heading: 2.1 Conversational Recommendation
text: Conversational recommendation is an emerging task where the main goal is to provide high-quality recommendations to users through natural language conversations. Compared to traditional recommendation tasks, conversational recommendation is a more challenging task as it requires the model to not only recommend appropriate items (i.e., recommendation) but also generate engaging and helpful responses (i.e., response generation).
To facilitate the study of conversational recommendation, several datasets have been proposed by previous work. Li et al. (2018) and Hayati et al. (2020) combine the elements of social chitchat and recommendation dialogues. Zhou et al. (2020b) collect human-machine conversation data guided by pre-defined topics. While most existing work has collected conversational recommendation data through crowdsourcing, those datasets are often not scalable and can suffer from less diverse user preferences and uninformative recommendations.
To handle this limitation, He et al. (2023) scrape single-turn recommendation dialogues from Reddit. However, their practical usability has been limited due to the low quality of scraped dialogues and lack of active interactions. Similar to our work, Lu et al. (2023) generate recommendation dialogues by converting user-item interactions into dialogues with a data-to-text generation model. Nonetheless, as the data-to-text model is trained on existing recommendation dialogues, the generated dialogues still inherit the previous limitations of the crowdsourced dataset. In contrast, we tackle the limitation by fully utilizing LLMs for collecting recommendation dialogues and leveraging user reviews to enhance the diversity and informativeness of collected dialogues.

heading: 2.2 Synthetic Data Generation
text: LLMs have been increasingly used to synthesize dialogue datasets (Kim et al., 2022; Lu et al., 2023; Chen et al., 2023; Kim et al., 2023; Chae et al., 2023). Kim et al. (2022) build Blended Skill BotsTalk using multiple agents grounded in target skills. Chen et al. (2023) generate dyadic and multiparty conversations with topic words and show they have comparable quality to human-authored conversations. There are recent approaches that utilize external sources for generating high-quality dialogues with LLMs (Li et al., 2022; Zhou et al., 2022; Kulhánek et al., 2021). Kim et al. (2023) sought to distill conversations from InstructGPT 175B using a commonsense knowledge graph. Compared to existing works, we are the first to utilize dynamic input sources for generating informative responses on a significantly large-scale, which allows us to encompass an exceptionally broad information of user preferences and item information.

heading: 3 PEARL Construction
text: We construct PEARL, a large-scale recommendation dataset covering diverse preferences and detailed item explanations through five steps: (1) grouping real-world reviews with two axes, which are user and item, (§3.1), (2) equipping a user simulator with preferences of a real-world user using reviews (§3.2), (3) infusing item knowledge extracted from reviews to a recommender simulator (§3.3), (4) inferring the simulators to derive a dialogue (§3.4), and (5) filtering dialogues (§3.5). While our dataset construction process is domainindependent, we validate it in the movie domain due to its extensive prior attention (Li et al., 2018; Hayati et al., 2020; Liu et al., 2020; Zhou et al., 2020b), making it easier to evaluate. The overview of PEARL construction process is shown in Figure 2 and an example of PEARL is in Table 2.

heading: 3.1 Constructing User-Review and Item-Review Databases
text: To grant realistic preferences to the user simulator and item knowledge to the recommender simulator, we collect authentic reviews by scraping movie and review data from IMDB website.3
For the user simulator, we construct a userreview database where a set of movie title, review text, and rating is grouped by the user who wrote the review. To clarify the preference and remove any noise from the raw review text, we transform the raw text into a high-level review text that focuses on the features the user likes and dislikes by using an LLM. By utilizing a high-level review text instead of the raw text, we can help our user simulator ground on refined text without noise and also filter out personal information in the raw reviews. The prompt is shown in Table 12.
For the recommender simulator which should provide proper recommendations based on rich knowledge about the items, we construct an itemreview database where a set of genre, director, cast, and review text is grouped per movie title. By utilizing reviews, we can obtain information about items that cannot be gathered by only utilizing metadata of items. Here, we select up to three most voted reviews on IMDB for efficiency, instead of using all collected reviews of the item. Then, we transform them into a high-level review text, similar to the user-review database construction process. The prompt is shown in Table 13. As a result, the two databases contain 11,839 movies, 68,709 users, and 221,242 reviews in total.

heading: 3.2 Persona-augmented User Simulator
text: Our user simulator uses GPT-3.5 (i.e., GPT-3.5-turbo-1106; Ouyang et al. (2022)), though in practice, a different model could be used. We explain how we compose persona and how the user simulator generates an utterance.
3https://www.imdb.com/
Persona components. We provide the user simulator with persona which includes three types of preferences: general, target, and responsive preferences. For the general preference, we combine three randomly selected review texts of a particular user from the user-review database. This combination represents the user’s overall movie taste throughout the dialogue. For the target preference, we select a movie title and its corresponding review text that the user has rated highly (at least 8 out of 10). The user simulator’s role is to consistently express the specific attributes of the movie, so that the recommender simulator can eventually suggest the target movie to the user simulator while having a conversation. Lastly, to facilitate the user simulator to provide realistic feedback when a movie is suggested, we obtain the user’s review text of the movie from the user-review database as reference.
Utterance generation. The goal of the user simulator is to express its detailed preferences consistent with the persona and eventually get the target item as the final recommendation. Given the task description Du, persona P , and the dialogue context Ctu = (u1, r1, u2, ..., rt) which consists of utterances up to t-th turn where ui and ri are utterances from user and recommender respectively, GPT-3.5 generates the next response ut by following the task description under zero-shot setting. Note that the responsive preference in the persona is dynamically obtained from the user-review database when an item is suggested in the middle of the dialogue.

heading: 3.3 Knowledge-augmented Recommender Simulator
text: To generate utterances of recommender with proper recommendations and explanations, we leverage an LLM and a retriever. The retriever first searches top-k context-relevant items, and the LLM performs a step-by-step reasoning to suggest an item the user would like the most based on the expressed preferences and item knowledge. We use GPT-3.5 as the LLM.
Item retrieval for recommendation. Given the dialogue context Ctr = (u1, r1, u2, ..., ut), the retriever in the recommender simulator searches context-relevant items among candidate items. Here, we limit the candidate pool to the items for which the user has written a review, as the user simulator of the next turn should be able to provide detailed feedback. In detail, with text-embedding-ada-002, we compute the similarity between the text embedding of the dialogue context and the text embedding of each item knowledge in the candidate pool, which includes the metadata (e.g., title, genre, director, and cast) and the review text of the item. Finally, the top-k texts of the retrieved item knowledge are given to the LLM as input. In order to prevent the dialogue from diverging, we gradually decrease k and forcefully include the target item knowledge in the retrieved pool from a specific turn we set.
Item recommendation with reasoning. For a corpus with proper recommendations and sufficient explanations, we utilize an LLM’s reasoning ability
to recommend the optimal item based on the dialogue context. Given the task description Du, the retrieved item knowledge I , and the dialogue context Ctr, an LLM generates the next recommender response rt. It first infers the user’s preference based on the dialogue context. Then, given the retrieved top-k texts of item knowledge, the LLM selects the most preferable item by the user. Lastly, the LLM generates an utterance that recommends the selected item with a plausible explanation. The prompt is shown in Table 15.

heading: 3.4 Dialogue Generation
text: Figure 2 shows the architecture for the interaction between the user and recommender simulators. We first initialize the dialogue context with an utterance pair (i.e., two dialogue turns), and then each simulator starts interacting based on the given utterances. To simulate the real-world recommendation conversation, we provide different inputs in each turn. On the user simulator side, we provide a persona to generate utterances with specific preferences. On the recommender simulator side, we use the retriever to find suitable items for the dialogue context. Then the LLM reasons about the user’s preference and chooses the most proper item for the user and recommends the item along with a detailed explanation based on the item knowledge.

heading: 3.5 Dialogue Filtering
text: Basic filtering. Starting with an initial set of dialogues sampled from GPT-3.5, we remove dialogues (1) where utterances are generated repeatedly, (2) where the user simulator explicitly mentions the target item, or (3) where the user accepts other items instead of the target item. Through these data filtering procedures, the gathered dialogues could be better utilized to investigate the conversational recommendation methods.
Preference-based filtering. We also filter out dialogues based on user preference to maintain dialogue consistency and enhance preference-based recommendations. Specifically, we (1) compute the natural language inference (NLI) score between the persona and user utterances, filtering out the dialogues that include any utterance that contradicts the persona, and (2) extract the recommender simulator’s guess of user preference to check for contradictions with user utterances. If there is any contradicted utterance, we consider that the recommender simulator fails to model the user’s preference and
discard such dialogues.
Final dataset. By applying a series of dialogue filtering, we obtain PEARL with 57.2K dialogues with more than 4k users and 9k items, where 22.5% of the initial dialogues are removed.

heading: 4 Experiments

heading: 4.1 Evaluation on Dataset Quality
text: Datasets. We conduct experiments on ReDial (Li et al., 2018) and INSPIRED (Hayati et al., 2020). ReDial is an English CRS dataset about movie recommendations, and is constructed through crowdsourcing workers on Amazon Mechanical Turk (AMT). Similar to ReDial, INSPIRED is also an English CRS dataset about movie recommendations, but with a smaller size. These two datasets
are widely used for evaluating CRS models.
Human evaluation. To assess the relative quality of PEARL compared to previous datasets, we conduct human evaluation through head-to-head comparison on Amazon Mechanical Turk, comparing PEARL with two widely used open-domain dialogue datasets: ReDial (Li et al., 2018) and INSPIRED (Hayati et al., 2020). We randomly sample 100 dialogues from each dataset and evaluate them according to six criteria: (1) user-control, (2) expertise, (3) specificity of user preference, (4) relevance, (5) flow naturalness, and (6) consistency. Judges are asked to select a better one between two given dialogues, regarding each criterion. Further details are in Appendix B.
Figure 3 and 4 summarizes the head-to-head comparison of PEARL and human-annotated datasets. Despite being fully machine-generated, human raters judge PEARL as better in quality compared to both ReDial and INSPIRED.
User preference analysis. We compare the specificity of user preferences across diverse CRS datasets in Table 3 as the capability to understand
specific user preferences is crucial in suggesting personalized and satisfactory recommendations. We measure it by concatenating all user utterances (i.e., preference) of a dialogue and obtaining the number of unique n-grams within it. According to Table 3, PEARL contains more unique expressions than ReDial (Li et al., 2018) or INSPIRED (Hayati et al., 2020), indicating PEARL has less generic and more specific user preferences.
To investigate the effect of utilizing persona in the data synthesis process, we additionally conduct an ablation study on how persona-augmentation yields more diverse and distinct preferences explicitly through user utterances. By comparing the inter-dialogue similarity (i.e., semantic similarity between the concatenated user utterances from two arbitrary dialogues) in Table 4, we observe that synthesizing a dialogue with persona input yields utterances with more distinct preferences that are less similar to each other.
Knowledge-augmented recommendation analysis. We compare the degree of richness in explanations of recommender utterances of ReDial, INSPIRED, and PEARL in Table 5 as it is an important factor for knowledgeable and explainable conversational recommender systems. To quantitatively measure the richness, we calculate number
of words used in a single recommender utterance in average. For ReDial which contains several consecutive recommender utterances, we combine them into one utterance as we calculate. Also, we show qualitative examples of each dataset in Table 6. While the utterances of Redial and INSPIRED are relatively shallow and brief, the utterance of PEARL explains about the item in a great detail which may enhance user satisfaction.
Data scale comparison. With 57,277 dialogues, PEARL is the largest in scale compared to existing crowdsourced conversational recommendation datasets (Table 3). It contains more than 500k utterances, each reflecting the preferences of real-world users, thereby providing a rich resource for training conversational recommender systems.
Cost & time-efficiency comparison. Synthesizing PEARL by utilizing the simulators grounded on persona and knowledge is significantly more efficient than traditional dialogue crowdsourcing datasets in both cost and time. For instance, INSPIRED dataset took four months to crowdsource a total of 1,001 dialogues. In contrast, our data generation process with GPT-3.5-turbo-1106 generates more than 57k dialogue datasets in just one week. Furthermore, in terms of cost, the INSPIRED dataset costs an average of $5 per dialogue, whereas the data synthesis process incurs a total cost of about $0.02 per dialogue.

heading: 4.2 Evaluation on Dataset Utility
text: Human evaluation. To qualitatively assess the utility of PEARL, we perform human evaluation that compares the responses of BART (Lewis et al., 2020) trained on ReDial (BART-ReDial) and on PEARL (BART-PEARL) given the same dialogue context from INSPIRED, which is an unseen
dataset for the models. We sample 100 dialogue contexts randomly from INSPIRED test set and ask three human judges per each dialogue context to select a better response between the two in terms of six distinct criteria: (1) fluency, (2) expertise, (3) explainability, (4) relevance, (5) naturalness, and (6) overall. Further details are in Appendix B.
Although PEARL is the only machine-generated dataset, Figure 5 shows that BART-PEARL consistently outperforms BART-ReDial across all evaluation criteria. Specifically, BART-PEARL shows the largest gap in terms of expertise, highlighting the utility of our approach in enriching recommender responses with a deeper understanding and insight into the movie domain.
Automatic evaluation. We conduct experiments on response generation and recommendation tasks to assess the utility of PEARL, following previous CRS works (Zhou et al., 2020a; Wang et al., 2022). For the response generation task, we employ diverse language models such as BART (Lewis et al., 2020), UniCRS (Wang et al., 2022), and PECRS (Ravaut et al., 2024) to evaluate how effectively PEARL enhances the diversity of the outputs. We evaluate the models using context from an unseen dataset, INSPIRED, and adopt Distinct-n (n=3,4) as the metric. For the recommendation task, BERT (Devlin et al., 2019), UniCRS, and PECRS are employed. We use Recall@k (k=1, 10, 50) as the metric, which indicates the percentage of target items correctly identified within the top-k recommendations. In addition to the downstream models, we also provide performances for zero-shot GPT-3.5 (GPT-3.5-turbo-1106) on both tasks to assess the capabilities of large language models in handling
these tasks without task-specific fine-tuning.4
Table 7 compares the response generation performances of models trained on PEARL and on ReDial, when evaluated on INPSIRED. The results show the effectiveness of PEARL in improving the diversity of responses generated by downstream models. Moreover, GPT-3.5 shows lower performances than UniCRS-PEARL and PECRS-PEARL, despite GPT3.5’s much larger model size, indicating PEARL is challenging and of high-quality.
Table 8 presents the recommendation performances of models trained and evaluated on PEARL. As the recommendation ability of a trained model highly depends on the distribution of the training data, we do not evaluate with an unseen dataset as done in response generation experiments. We find all models exhibit low performances, indicating that PEARL presents a significant challenge. Interestingly, GPT-3.5 shows a poor performance, suggesting that although PEARL is generated by the same model (i.e., GPT-3.5), there is no inherent advantage for the model. This observation underscores the need for future research to focus on developing models specifically tailored to PEARL, which leverages real-world data rather than relying on parametric knowledge.

heading: 4.3 Case Study
text: Table 9 and 10 present cherry-picked and lemonpicked responses from BART-ReDial and BART-
4GPT-3.5 is evaluated in zero-shot setting on recommendation and response generation tasks following He et al. (2023).
PEARL, respectively. BART-PEARL consistently provides explanations that elucidate what the recommended item is and why the recommender suggests the item, while BART-ReDial offers shorter responses without such details. In the lemonpicked example, BART-PEARL seems to forgot the previous mention of “The Avengers (2012)”. However, it still manages to explain the recommended item, demonstrating the model’s explainability trained on our synthesized dataset.

heading: 5 Conclusion
text: In this work, we introduce PEARL, a largescale conversational recommendation dataset constructed using LLM simulators augmented with persona or knowledge from real-world reviews. Our comprehensive experiments validate PEARL’s superior quality and utility in training models compared to existing datasets.
PEARL paves the way for future research in developing effective conversational recommender systems, particularly those emphasizing explainability, knowledge retrieval, and reasoning abilities. Also, PEARL offers a practical opportunity for developing small and deployable systems capable of handling specific user feedback and providing satisfactory recommendations.
Limitations
As we generate recommendation dialogues using LLM-based simulators, the choice of a language model (i.e., GPT-3.5 in this work) will impact the quality of dialogue created. One of the possible future directions may include curating recommendation dialogues by using simulators based on different language models and investigating the difference between generated dialogues and utterances.
Ethical Considerations
Our work utilizes a large language model and realworld reviews for recommendation dialogue generation and filters out generated dialogues in terms of user preference. As our filtering mechanism does not address considerations related to dialogue safety, the users who employ our data generation process should be mindful of this limitation and consider the incorporation of additional filtering steps to mitigate potential biases or toxic content. We ensure that workers hired through Amazon Mechanical Turk receive fair compensation. We offer an effective hourly rate exceeding $15, based on the estimated time required to complete the tasks.

heading: Acknowledgements
text: This work was supported by the IITP grant funded by the Korea government (MSIT) (No. RS-2020II201361) and the NRF grant funded by the Korea government (MSIT) (No. RS-2023-00244689). We thank Hyunseo Kim and Soyeon Chun for their participation in experiments

heading: B Human Evaluation Metrics
text: B.1 Dataset Quality
We outsource a human evaluation comparing our PEARL and previous datasets via Amazon Mechanical Turk (AMT). We show the interface for the evaluation in Figure 7. We ask the human judges to compare the dialogues based on the following criteria:
5https://openai.com/blog/chatgpt
• User-control: Which seeker participates more actively and provides feedback to receive recommendations in dialogues?
• Expertise: Which recommender seems to be more of an expert in the movie domain?
• Specificity: Which seeker is better at expressing preferences that help the recommender suggest a personalized movie?
• Relevance: Which dialogue provides more relevant recommendations according to the seeker’s preferences?
• Flow Naturalness: Which one is more natural in the progression of the dialogue?
• Consistency: Which dialogue is more consistent in terms of the seeker’s preferences?
During each stage of voting, human judges are given two dialogue candidates and asked to judge which one is of a higher quality based on the above criteria.
B.2 Dataset Utility We also outsource a human evaluation comparing the responses of the BART model trained on PEARL and ReDial dataset. We show the interface for this evaluation in Figure 8.
We ask the human judges to compare the responses and select the better one based on the following criteria:
• Fluency: Which response is more fluent?
• Expertise: Which response seems to have more expertise in the movie domain?
• Explainability: Which response offers more sufficient explanations with the recommendations?
• Relevance: Which response provides more relevant recommendation according to the seeker’s preference?
• Naturalness: Which response is more natural?
• Overall: Which response do you prefer overall?

heading: C Additional Experimental Results
text: Dataset utility for practical use Our research aims to tailor a model specific to the conversational recommendation task, by utilizing our proposed dataset. While a large language model (e.g.GPT3.5) may seem plausible as a conversational recommender due to its capability to generate human-like text, PEARL and its downstream models are still necessary. First of all, a large language model (e.g.GPT-3.5) is practically inappropriate to be deployed in real-time services due to its latency, cost, and bias issues. Also, while constructing the dialogues in PEARL, we have taken into account the relationship between the actual user history and the the target recommendation. We believe that large language models lack in such collaborative interaction knowledge, as shown in Table 8 where it achieves only 0.0355 in Recall@10 in our test set.
In addition, we ran a human evaluation between responses of GPT-3.5 and BART trained on PEARL(BART-PEARL), given INSPIRED contexts. We randomly sampled 50 responses from each model and used the same criteria as dataset utility human evaluation in the paper. As shown in Figure 6, responses from BART-PEARL are generally preferred to those from GPT-3.5. Although
BART shows inferior fluency and decent relevance, it outperforms GPT-3.5 in expertise and explainability, the two most important aspects emphasized throughout our paper. We believe that augmenting responses with domain-specific knowledge in PEARL helped the downstream model enrich explanations beyond the basic information of the movie.
Explainability of downstream models Table 11 shows experimental results for downstream models (BART, UniCRS, and PECRS) evaluated on E-ReDial (Guo et al., 2023). E-ReDial consists of high-quality explanations manually annotated by human workers, based on ReDial dialogues. To asses the models’ ability in providing explanations for recommendations, we utilize the Test-Rec subset of the test set, which always contains recommendations and explanations as suggested by the authors.
According to Table 11, models trained on PEARL outperforms those trained on ReDial on most metrics. For the reference-based metrics (ROUGE-1, ROUGE-2) where they underperform, we speculate that models trained on ReDial benefit from sharing the recommendation distribution with E-ReDial.

heading: Future Work
text: PEARL paves the way for future research in developing effective conversational recommender systems, particularly those emphasizing explainability, knowledge retrieval, and reasoning abilities. Also, PEARL offers a practical opportunity for developing small and deployable systems capable of handling specific user feedback and providing satisfactory recommendations. Limitations
As we generate recommendation dialogues using LLM-based simulators, the choice of a language model (i. e. , GPT-3. 5 in this work) will impact the quality of dialogue created. One of the possible future directions may include curating recommendation dialogues by using simulators based on different language models and investigating the difference between generated dialogues and utterances. Ethical Considerations
Our work utilizes a large language model and realworld reviews for recommendation dialogue generation and filters out generated dialogues in terms of user preference. As our filtering mechanism does not address considerations related to dialogue safety, the users who employ our data generation process should be mindful of this limitation and consider the incorporation of additional filtering steps to mitigate potential biases or toxic content. We ensure that workers hired through Amazon Mechanical Turk receive fair compensation. We offer an effective hourly rate exceeding $15, based on the estimated time required to complete the tasks