# Spars of Artificial General Recommender (AGR): Experiments with ChatGPT
## 2 AGR Principles
### Acquisition Strategy:
In the latter scenario, the system should be able to generate educated conjectures about why the recommended outputs are undesired, according to all information collected during the dialogue. * Testing Protocol: We intentionally provide negative feedback in two forms: explicit natural language-based explanations detailing the dissatisfaction, and implicit expressions of dissatisfaction without accompanying reasons. In cases of explicit feedback, we prompt the model to generate an alternative recommendations, accompanied by a rationale, and observe whether the newly recommended items addresses all concernsraised in the feedback. For implicit feedback, the model is assessed on its ability to infer the underlying causes of dissatisfaction using contextual and user information. **Entity Inference:**  
The system should be designed to handle situations where users struggle to remember the exact name or title of an item they want to discuss during a conversation, but can recall specific attributes or details [26]. In such instances, the system should be able to make informed guesses, providing a brief summary for each proposed item based on the available information, and seek user confirmation. This functionality should be maintained even when the user-supplied details contain errors or inaccuracies. * Testing Protocol: To assess the model's ability to infer the intended items mentioned by the user based on limited details, we selected a number of movies on Amazon in order to simulate items that users may have forgotten. We then request the model to provide an informed guess for the forgotten item by providing a certain amount of metadata and/or item description. Note that the guessed item can still be considered qualified if the model proposes a similar item that fulfills the intended functionality of the reserved item and aligns with the provided item description, even if it fails to identify the exact item. **Behavioral Analysis:**  
The system should exhibit the capability to systematically examine user interaction history to identify potential alternations in user behavior [27], thus recognizing the evolution of user preferences, such as transitions in preferred movie genres as one ages. This functionality allows the system to capture \"at-the-moment\" user preferences and offer contextually pertinent recommendations in real-time. Additionally, the system should possess the capacity to propose supplementary facets beyond the ones from item metadata, to facilitate a more in-depth analysis of user preferences. * Testing Protocol: We propose interaction histories that include facet-based behavior changes, such as transitioning from purchasing items of one brand or franchise to another or switching from a preferred size to an alternative. We then examine the system's capacity to detect potential shifts and/or expansions in user preferences when prompted. Additionally, we prompt the model to propose supplementary facets and conduct preference elicitation based on these additional aspects, then observe the rationale behind their utterances. **Inconsistency Detection:**  
This principle aims at addressing the three major types of inconsistencies: logical, expectational, and factual, as defined by [28]. Specifically, the system should be able to accurately detect user made inconsistent statements that fall under each of the three aforementioned types within a dialogue to establish system accountability. Additionally, it should provide explanations for the detected inconsistencies, while incorporating an appropriate amount of contextual information derived or cited from user inputs. * Testing Protocol: To assess the model's capacity to detect inconsistencies in user statements for each type, we design three distinct scenarios: (1) Logical inconsistency: for this scenario, we first provide the model with the interaction history and/or metadata of a sampled user, followed by statements that clearly contradict the previously provided information.