INSPIRED : Toward Sociable Recommendation Dialog Systems (2020)

Abstract: In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge when developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies. Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs. Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations. Based on our dataset, we train end-to-end recommendation dialog systems with and without our strategy labels. In both automatic and human evaluation, our model with strategy incorporation outperforms the baseline model. This work is a first step for building sociable recommendation dialog systems with a basis of social science theories.

## introduction sociable conversational agents build rapport with users, in order to gain trust and favor from them. social science researchers believe that the rapport influence a more persuasive recommendation to successfully suggest an item that satisfies user needs @xcite @xcite . however, existing works on recommendation dialog systems lack a study about communication strategies used by human speakers for making successful and persuasive recommendations. they collect the dataset in scenario-based settings or convert product review datasets into question-answering conversations @xcite @xcite @xcite . common issues with these types of datasets are: (1) homologous utterances, (2) mostly question-answering pairs, and (3) lack of user engagement. in this work, we aim to validate whether sociable recommendation strategies are effective for making a successful recommendation in a dialog. to do so, dataset inspired convrec gorecdial redial @xcite @xcite ## recommendation dialog systems to evaluate how the strategies in inspired are useful in creating a more engaging and persuasive recommendation dialog, we develop a generative dialog model as our baseline to compare against our strategy-incorporated dialog system. we split the dialogs into 801/100/100 for train/validation/test split. we use external recommendation system from tmdb foot_2 with heuristics to select the movies. more details for heuristics and training set-up are in the appendix. ## conclusion and future work in this work, we have introduced inspired, a new recommendation dialog dataset collected in natural setting and annotated with sociable recommendation strategies. we analyze the connection between different strategies and the recommendation results. our findings show that sociable strategies do have a positive impact on the acceptance of recommendation and dialog quality. this work opens up several directions for future studies in building sociable and personalized recommendation dialog systems as follows: first, we will explore more ways of utilizing the strategies, including dynamic strategy selection after decoding. then, we plan to investigate the strategy patterns for people with different personalities and movie preferences to make dialog system more personalized. finally, another interesting exploration is to extend the model with a jointly trainable movie recommendation and movie information modules.