Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems (2002.09102v1)

Abstract: Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. We argue that three fundamental problems need to be solved: 1) what questions to ask regarding item attributes, 2) when to recommend items, and 3) how to adapt to the users' online feedback. To the best of our knowledge, there lacks a unified framework that addresses these problems. In this work, we fill this missing interaction framework gap by proposing a new CRS framework named Estimation-Action-Reflection, or EAR, which consists of three stages to better converse with users. (1) Estimation, which builds predictive models to estimate user preference on both items and item attributes; (2) Action, which learns a dialogue policy to determine whether to ask attributes or recommend items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage. We present two conversation scenarios on binary and enumerated questions, and conduct extensive experiments on two datasets from Yelp and LastFM, for each scenario, respectively. Our experiments demonstrate significant improvements over the state-of-the-art method CRM [32], corresponding to fewer conversation turns and a higher level of recommendation hits.

Estimation--Action--Reflection: A Framework for Conversational Recommender Systems

The paper under discussion presents an innovative framework, named Estimation–Action–Reflection (EAR), designed to enhance the interactivity and adaptability of Conversational Recommender Systems (CRS). This framework addresses three pivotal challenges in CRS: determining the optimal questions to inquire about user preferences, deciding the appropriate moments to proffer recommendations, and assimilating user feedback to refine suggestion models. By tackling these issues, EAR significantly improves the synergy between conversational interfaces and recommendation algorithms.

Key Contributions

The research delineates a three-stage methodology to streamline the interaction between conversational agents and recommender mechanisms:

1. Estimation: This phase focuses on predicting user preferences, leveraging factorization machines (FM) to evaluate both item and attribute affinities. A key innovation here is the attribute-aware Bayesian Personalized Ranking, which refines item predictions by incorporating attribute considerations. The model's efficacy is further enhanced through multi-task training, which aligns the dual goals of item and attribute preference estimation.
2. Action: At this stage, the system decides among possible actions—whether to ask a user about specific attributes or to make item recommendations. The decision process is guided by a state vector comprising multiple facets: attribute entropy, predicted attribute preference, dialogue history, and the candidate item list length. This nuanced state representation ensures a more informed and strategic action selection process, facilitated by a policy network optimized via reinforcement learning.
3. Reflection: The reflection phase involves real-time adaptation of the recommendation model based on user feedback, particularly when recommendations are rejected. This involves updating the FM with newly constructed training examples, ensuring that the model remains responsive and accurate in dynamic recommendation scenarios.

Experimental Evaluation

The framework's efficacy was substantiated through experimentation on two distinct datasets—Yelp and LastFM—each featuring different interaction scenarios: enumerated and binary questioning, respectively. The authors' experiments reveal EAR's superiority over baseline methods, such as CRM and other attribute-based strategies, in terms of achieving higher success rates and reduced average interaction turns. Notably, the framework's structured approach to leveraging both estimated preferences and conversation history aids in strategizing action sequences conducive to effective recommendations.

Implications and Future Directions

From a practical standpoint, EAR's structured interaction model facilitates more effective deployment of CRSs in domains where dynamic user preference capture is paramount. The ability to seamlessly incorporate and adapt to user feedback in real-time promotes a personalized user experience, potentially increasing user satisfaction and system efficacy.

Theoretically, this research opens avenues for further exploration of state representation and decision-making strategies in interactive systems. Additionally, the findings suggest potential enhancements in online model updating techniques, which can be further optimized to avoid detrimental updates.

For future developments, the applicability of EAR in more expansive settings involving diverse question types or multi-task interactions can be explored. Furthermore, deploying the system in live environments can yield insights into real-world user behaviors and interaction patterns that are not fully reproducible in simulated settings, thereby refining the approach and strengthening its generalizability and robustness.

In summary, EAR provides a comprehensive solution for addressing integral challenges in conversational recommendation tasks, rendering it a valuable contribution to the continuous advancement of interactive AI systems. This framework highlights the significance of effective integration between conversational and recommendation modules, setting a precedent for future research in this vibrant field.