Generating Recommendation Dialogues from Product Models

AAAI Conferences

For complex and frequently changing product domains, the maintenance of an electronic recommender system is a timeand money-consuming task, as the man-machine interface has to be adapted to the product model any time the latter is changed. Ideally, changes in the model would lead to an automatic adaptation of the recommendation dialogue without much overhead. In this paper we present an approach to generate an elaborate dialogue from a given product model, ensuring efficient reaction to changes of the model. Using statecharts to structure the dialogue, an intuitive, easily visualizable internal representation can be inferred that is able to handle all principal functions required of a recommender system, such as dynamic dialogue management, belief revision and the generation of recommendations.


Recommending from Experience

AAAI Conferences

In this paper we present RC, a context-driven recommender system that mines contextual information from user-generated reviews and makes recommendations based on the users' experiences. RC mines the contextual information from the user-generated reviews using a form of topic modeling. This means that, unlike other context-aware recommender systems, RC does not have a predefined set of contextual variables. After mining the contextual information, RC makes top-n recommendations using a Factorization Machine with the contextual topics as side information. Our experiments on two datasets of ratings and reviews show that RC has higher recall than a conventional recommender.


Towards Deep Conversational Recommendations

Neural Information Processing Systems

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.


Towards Deep Conversational Recommendations

Neural Information Processing Systems

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.


Towards Deep Conversational Recommendations

arXiv.org Machine Learning

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.