Can conversational recommenders recover lost preference signals from history?

Conventional CRS infers user preferences from the current dialogue session. UCCR's argument is that this inherits an amputation from earlier CRS architectures: traditional recommenders use both item-CF (a user's history of items, what they tend to like over time) and user-CF (similar users, whose preferences predict yours). When CRS focused on the dialogue, both channels were dropped — even though they remain informative.

The remediation: model preferences from three sources. The current session captures immediate intent. Historical dialogues capture the user's stable preferences across time, an item-CF analog. Look-alike users — retrieved by profile similarity or behavior similarity — provide a user-CF supplement, especially valuable when the current session is sparse or vague.

The non-trivial integration challenge is conditioning the historical and look-alike features on the current intent. If the user just said "I want a comedy", historical preferences for thrillers should be downweighted relative to historical preferences for comedies. The multi-view preference mapper learns intrinsic correlations between word-level semantic, entity-level knowledge, and item-level consuming views via self-supervised cross-view objectives — different views of the same user should be more correlated than views of different users.

The architectural claim is that CRS lost ground by becoming dialogue-focused, and recovering item-CF and user-CF channels (carefully integrated with current intent) brings CRS back to the recommendation field's accumulated knowledge about user representation. The mechanism is straightforward; the lesson is methodological: when a subfield drifts from the parent field's primitives, check whether the drift was justified or whether useful structure was discarded.

Source: Recommenders Conversational