RevCore: Review-augmented Conversational Recommendation

CR remains challengeable because (i) typical dialogues are short and lack sufficient item information for user preference capturing (Chen et al., 2019; Zhou et al., 2020), and (ii) difficulties exist in generating informative responses with item-related descriptions (Shao et al., 2017; Ghazvininejad et al., 2018; Wang et al., 2019b). Thus, recently, external information in the form of structured knowledge graphs (KG) is introduced to enhance item representations by using rich entity information in KG (Chen et al., 2019; Zhou et al., 2020). While KGbased methods improve CR to some extent, they are still limited in (i) worse versatility resulted from a high cost of KG construction; and (ii) inadequate integration of knowledge and response generation (Lin et al., 2020).

resulting in an uninformative response “It’s great.”, thus the chat does not help with recommendation owing to lacking necessary knowledge.

Review-augmented Conversational Recommender (RevCore), to enhance CR by additional review data. In doing so, we firstly analyze user’s utterances with their sentiment polarities and then retrieve reviews for the items mentioned by the user with keeping their sentiment matching the utterances (e.g., they should be both positive or negative). The obtained reviews are thus recommendation-beneficial (He et al., 2015; Hariri et al., 2011) because they are given by the ones who have seen/used and also show interests (or with no interests) in the mentioned items. Afterward, we incorporate the selected reviews into dialogue history, from which the CR system can learn user preference from review-enriched item information. In addition, we also use the sentiment coordinated reviews to enhance the dialogue response generation, where a review-attentive decoder introduces item information from selected reviews to generate coherent and informative responses. To the best of our knowledge, it is the first time that the aforementioned CR issues have been addressed through incorporating external reviews. Experimental results on a widely used benchmark dataset (Li et al., 2018) show that RevCore is superior on both recommendation accuracy and conversation quality. Further analyses are also performed to confirm the effectiveness of RevCore in an appropriate manner of introducing reviews to CR.