Towards Conversational Collaborative Recommendation

Traditionally, collaborative recommender systems have been
based on a single-shot model of recommendation where a single set of
recommendations are generated based on a user's (past) stored preferences.
However, content-based recommender system research has begun
to look towards more conversational models of recommendation, where
the user is actively engaged in directing search at recommendation time.
Such interactions can range from deep dialogues with the user that may
involve natural language dialogues, to more simple interactions where
the user is, for example, asked to indicate a preference for one of k suggested
items. Importantly, the feedback attained from these interactions
can help to di erentiate between the user's long-term stored preferences,
and her current (short-term) requirements, which may be quite di erent.
We argue that such interactions can also be bene cial to collaborative
recommendation and provide preliminary experimental evidence in support
of this.