# Spars of Artificial General Recommender (AGR): Experiments with ChatGPT
## 2 AGR Principles
### Acquisition Strategy:  
The system should aim to strike a balance between the number of questions posed and the relevance of the provided recommendations by efficiently collecting all necessary information for precise recommendations while avoiding excessive user fatigue arising from multi-turn dialogues [21]. One potential approach could involve employing various heuristics to consistently calculate and identify the most informative questions to present, thereby maximizing information gain while minimizing the user's cognitive burden [22]. Testing Protocol: To comprehend the underlying rationale behind the model's selection of questions, we engage in recommendation-oriented dialogues with the model and probe its reasoning for choosing particular questions. Simultaneously, we prompt the model to propose alternative questions that are also relevant to the subject and inquire as to why the model did not choose such queries. We then proceed to assess the explanations offered by the model in response to our inquiries. Contextual Memory:During dialogues, users may frequently refer to prior stated items without providing a full description [17]. For example, in phone recommendations, users may pose questions such as: \"How does this phone compare to the ones I mentioned earlier?\" To enhance the continuity and naturalness of such interactions, the system should be able to store and retrieve such referential entities for potential future use [23]. Additionally, other user-related information, such as metadata and interaction history, should also be stored and recalled if necessary. Lastly, the system should demonstrate the ability to remember its own previous statements within an ongoing dialogue. * Testing Protocol: To assess the model's capacity for retaining and employing previously presented information, we engage in dialogues with the model. During the initial round, we provide the user's metadata and/or interaction history. We then test the model's ability to recollect this information after a certain number of rounds. We also prompt the model to output its prior utterances and inspect whether any statements are forgotten. Moreover, we introduce a referential entity, such as an additional item or a friend of the user, at the beginning of the dialogue. Later in the conversation, we refer back to this entity for comparative purposes and examine the model's capacity for restoring and utilizing all the information from the entity to respond to user inquiries. Repair Mechanism:Given the possibility of modifications in user-stated information at a later stage of the dialogue, it is imperative to incorporate a repair mechanism [17; 24]. This feature enables users to provide supplementary details, amend inaccuracies, or even remove previous utterances. To accommodate these adjustments, the system must be designed to generate suitable recommendations and explanations based solely on the updated information. * Testing Protocol: To examine the model's ability to adapt to changes stemming from previous user statements, we refer back to previously provided metadata and/or user interactions and make modifications to them in subsequent rounds of the conversation. The model's capacity to discard outdated data while utilizing only the modified information for recommendation and reasoning will then be observed. Feedback Mechanism:Users may struggle in explicitly articulating the rationale behind their dissatisfaction with the recommended items [25]. This predicament poses an obstacle for the system in acquiring user preferences from recommendations it may propose throughout the dialogue. To mitigate this issue, the system should be designed to make adjustments based on both feedback that convey reasons for the undesirability of the recommended items, as well as feedback that merely states that the items are unsatisfactory without any providing any explanations.