Conversational Recommender Systems assist online users in their information-seeking and decision making tasks by supporting an interactive process with the aim of nding the most appealing items according to the user preferences. Unfortunately, collecting dialogues data to train these systems can be labour-intensive, especially for data-hungry Deep Learning models. Therefore, we propose an automatic procedure able to generate plausible dialogues from recommender systems datasets.
the knowledge base contains the triple (item, predicate, object), 0 otherwise. The considered predicates are wdt:P57 (director), wdt:P161 (cast member), wdt:P136 (genre) 4. The dialogue generation procedure is an iterative algorithm which is executed until all user preferences have been used. At each step of the dialog generation procedure, a top-n list of items composed by positive and negative items is generated by randomly choosing from positive and negative preferences of the given user u. Then, paths from the root of the decision tree to the consistently classi ed examples are exploited to generate a sequence of questions, randomly chosen according to a binomial distribution over the item features, to elicit user preferences. Depending from the percentage of positive items in the top-n, a \re ne" step is triggered which extends the dialog with additional questions that lead to a list of suggestions which contains only positive items.
Table 1 shows a conversation generated by applying the designed procedure to the well-known MovieLens 1M recommender systems dataset. In the rst part of the conversation, utterances with the aim of introducing the user are generated by exploiting the contextual information included in the dataset.
In this work we have proposed an automatic procedure able to generate synthetic dialogue datasets starting from well-known datasets in the recommender system eld. The presented procedure is completely generic and can be applied on any dataset containing binary user preferences and whose items have a corresponding identi er in the Linked Open Data Cloud. 4 The pre x wdt stands for http://www.wikidata.org/prop/direct/