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State-of-the-art methods on conversational recommender systems (CRS) leverageexternal knowledge to enhance both items' and contextual words' representationsto achieve high quality recommendations and responses generation. However, therepresentations of the items and words are usually modeled in two separatedsemantic spaces, which leads to misalignment issue between them. Consequently,this will cause the CRS to only achieve a sub-optimal ranking performance,especially when there is a lack of sufficient information from the user'sinput. To address limitations of previous works, we propose a new CRS frameworkKLEVER, which jointly models items and their associated contextual words in thesame semantic space. Particularly, we construct an item descriptive graph fromthe rich items' textual features, such as item description and categories.Based on the constructed descriptive graph, KLEVER jointly learns theembeddings of the words and items, towards enhancing both recommender anddialog generation modules. Extensive experiments on benchmarking CRS datasetdemonstrate that KLEVER achieves superior performance, especially when theinformation from the users' responses is lacking.

Improving Items and Contexts Understanding with Descriptive Graph for Conversational Recommendation