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Outline

Title
Abstract
Introduction and Motivation
Personalized Conversational Recommendation Systems
Conversational Recommendation
Experimental Variables
Experimental Results
Discussion
Related Research
Directions for Future Work
Conclusions
References
All Topics
Computer Science
Artificial Intelligence

A personalized system for conversational recommendations

Profile image of Mehmet GökerMehmet Göker

2004, Journal of Artificial Intelligence …

Abstract

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -the Adaptive Place Advisor -that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.

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Fifteenth ACM Conference on Recommender Systems, 2021

Recommender systems have demonstrated great success in information seeking. However, traditional recommender systems work in a static way, estimating user preferences on items from past interaction history. This prevents recommender systems from capturing dynamic and fine-grained preferences of users. Conversational recommender systems bring a revolution to existing recommender systems. They are able to communicate with users through natural language, which enables them to explicitly elicit user preferences by asking whether a user likes an attribute or item or not. Based on information shared through users' responses, a recommender system can produce more accurate and personalized recommendations. We identify five emerging trends in the general area of conversational recommender systems: (1) Question-based user preference elicitation; (2) Multi-turn conversational recommendation strategies; (3) Dialogue understanding and generation; (4) Exploitationexploration trade-offs; and (5) Evaluation and user simulation. This tutorial covers these five directions, providing a review of existing approaches and progress on each topic. By presenting the emerging and promising topic of conversational recommender systems, we aim to provide take-aways to practitioners to build their own systems. We also want to stimulate more ideas and discussions with audiences on core problems of this topic such as task formalization, dataset collection, algorithm development, and evaluation, with the ambition of facilitating the development of conversational recommender systems.

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Current recommender systems, based on collaborative filtering, implement a rather limited model of interaction. These systems intelligently elicit information from a user only during the initial registration phase. Furthermore, users tend to collaborate only indirectly. We believe there are several unexplored opportunities in which information can be effectively elicited from users by making the underlying interaction model more conversational and collaborative. In this paper, we propose a set of techniques to intelligently select what information to elicit from the user in situations in which the user may be particularly motivated to provide such information. We argue that the resulting interaction improves the user experience. We conclude by reporting results of an offline experiment in which we compare the influence of different elicitation techniques on both the accuracy of the system's predictions and the user's effort.

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Personalized Conversational Case-Based Recommendation
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The purpose of a Conversational Recommender System is to help the users achieve their recommendation specific goals using a multi-turn dialogue. In recent years, numerous studies are conducted on improving the quality attributes of a conversational recommender system. Multiple conversational movie recommender systems are proposed. However, there is a need for a conversational system for a movie recommendation, which can be used for research purposes. The main goal of this thesis is to create Jarvis, an open-source, rule-based conversational movie recommender system focusing on understanding the users’ goals and adapting to their changing requirements. In order to understand the users’ goals, a database is created, which contains the attributes with higher coverage of possible users’ goals. A multi-model chat interface is designed for Jarvis. This interface introduces the components for better user interaction and providing users a guide during the conversation. The success of a conv...

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Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user preference. In a typical conversational recommendation scenario, a CRS firstly generates questions to let the user clarify her/his demands and then makes suitable recommendations. Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations. However, existing CRSs fall short in asking high-quality questions because: (1) system-generated responses heavily depends on the performance of the dialogue policy agent, which has to be trained with huge conversation corpus to cover all circumstances; and (2) current CRSs cannot fully utilize the learned latent user profiles for generating appropriate and personalized responses. To mitigate these issues, we propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation. Distinct from previous conversational recommender systems, KBQG models a user's preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph (KG). Conditioned on the varied importance of different relations, the generated clarifying questions could perform better in impelling users to provide more details on their preferences. Finially, accurate recommendations can be generated in fewer conversational turns. Furthermore, the proposed KBQG outperforms all baselines in our experiments on two real-world datasets. CCS CONCEPTS • Information systems → Users and interactive retrieval; • Computing methodologies → Knowledge representation and reasoning.

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2014

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Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
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Proceedings of the 13th International Conference on Web Search and Data Mining

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. CCS CONCEPTS • Information systems → Users and interactive retrieval; Recommender systems; Personalization; • Human-centered computing → Interactive systems and tools.

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A Framework for Building Chat-based Recommender Systems
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2018

Chat-based recommender systems are getting more and more attention in recent time given their natural interaction with the user. Indeed, chat-based recommender systems implement a paradigm where users define their preferences and discover items that best fit their needs through a dialog. A chat-based recommender system can be easily integrated in platforms such as social networks, e-commerce websites, bank websites. Therefore, the preferences can be directly provided by the users during the dialog or can be automatically extracted from their activities on the same platform that hosts the chatbot [3]. In this demo, we present a framework for building chatbased recommender systems. The framework, based on a content-based recommendation algorithm, is independent from the domain.

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Pontus Wärnestål

… of the 8th SIGDIAL Workshop on …, 2007

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Compound critiques for conversational recommender systems
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