Knowledge-Grounded Natural Language Recommendation Explanation (2023)

Abstract: Explanations accompanying a recommendation can assist users in understanding the decision made by recommendation systems, which in turn increases a user’s confidence and trust in the system. Recently, research has focused on generating natural language explanations in a human-readable format. Thus far, the proposed approaches leverage item reviews written by users, which are often subjective, sparse in language, and unable to account for new items that have not been purchased or reviewed before. Instead, we aim to generate fact-grounded recommendation explanations that are objectively described with item features while implicitly considering a user’s preferences, based on the user’s purchase history. To achieve this, we propose a knowledge graph (KG) approach to natural language explainable recommendation. Our approach draws on user-item features through a novel collaborative filtering-based KG representation to produce fact-grounded, personalized explanations, while jointly learning user-item representations for recommendation scoring. Experimental results show that our approach consistently outperforms previous state-of-the-art models on natural language explainable recommendation metrics.

## introduction current approaches to natural language (nl) explainable recommendation focus on generating user reviews @xcite @xcite @xcite . instead of providing a justification for the item recommendation, the models learn to output language that is commonly found in personal reviews. this reliance on reviews poses three problems: 1) the explanations are not objective, because users typically review items based on their sentiment @xcite , 2) reviews are often sparse, because they describe a user's own experience @xcite , 3) systems that rely on reviews cannot account for new items which have never been purchased before, nor can they provide justifications for item catalogs which may not have reviews available. given this, it may be difficult for a user to reason as to why an item was recommended, hindering the user's experience @xcite . the user may then lose trust in such systems which do not provide objective and accurate explanations. we propose knowrec, a kg-grounded approach to natural language explainable recommendation which not only personalizes recommendations/explanations with user information, but also draws on facts about a particular item via its corresponding kg to generate objective, specific, and data-driven explanations for the recommended item. for example, given the movie "paths of glory", previous work aims to generate explanations such as "it's not the best military movie" and "good performances all around", which are subjective, not specific to a given movie, and relies on data from pre-existing reviews. instead, by leveraging an item kg such @xmath0, stanley kubrick>, <conflict, world war 1>, <country, france>, a more objective and precise explanation can be produced such as: "a world war i french colonel defends three soldiers. directed by stanley kubrick." the item features of 'world war i', 'colonel', and 'defends three soldiers' in the explanation objectively describe the movie, while they can implicitly reflect the user's preferences for war movies, based on his/her purchase history. knowrec is also more advantageous than prior work in terms of scalability to unpurchased items. previously, kg-based recommendation systems have effectively addressed the cold-start problem by linking users and items through shared attributes @xcite @xcite . similarly, there exists a kind of cold-start problem for new items in recommendation explanation that rely on reviews. knowrec demonstrates kgs can help solve this problem through existing item-level features by adapting kg-to-text @xcite @xcite elements into explainable recommendation, producing item-level explanations to justify a purchase. the kg-based approach is particularly important for recommendation scenarios in special domains where personal reviews are not available and the review-based approaches are impractical. our approach presents several algorithmic novelties. first, inspired by work on kg recommendation @xcite and @xcite , we devise a novel user-item kg lexical representation, viewing the input through collaborative filtering lens, where users are graphically represented via their previous purchases and connected to a given item kg. our representation differs from previous work on explainable nl generation which relies on id and sparse keyword features. previous work extracts keywords from reviews to represent the user and item, linearizing all such features to encode and produce an nl explanation @xcite . next, knowrec adapts a graph attention encoder for the user-item representation via a new masking scheme. finally, the encoded kg representation is simultaneously decoded into a textual explanation, while we innovatively dissociate the joint learned user-item representation to compute a user-item similarity for recommendation scoring. to evaluate our approach, we first devise a method of constructing (kg, t ext) pairs from product descriptions as described in section 5, where we extract entities and relations for the item kgs. we construct two such datasets from the publically available recommendation datasets to evaluate our proposed model for both the explanation and recommendation task and focus on natural language generation (nlg) metrics for the explanation task as in previous work. we adapt and compare previous baseline models for the recommendation explanation task as described in section 6, where we substantially outperform previous models on explanation while achieving similar recommendation performance as models that rely on user and item id-based features. ## problem formulation following prior work, we denote u as a set of users, i as a set of items, and the user-item interaction matrix as @xmath1, where y @xmath2. here, we represent user u as the user's purchase history @xmath3, where v ui denotes the i-th item purchased by user u in the past. next, we define a kg as a multirelational graph @xmath4, e), where v is the set of entity vertices and e ⊂ v×r×v is the set of edges connecting entities with a relation from r. each item v has its own kg, g v , comprising an entity set v v and a relation set r v which contain features of v. we devise a set of item-entity alignments @xmath5, e)|@xmath6, @xmath7, where (v, e) indicates that item v is aligned with an entity e. given a user u and an item v represented by its kg g v , the task is to generate an explanation of natural language sentences e u,v as to why item v was recommended for the user u. as in previous multi-task explainable recommendation models, knowrec calculates a rating score r u,v that measures u's expected preference for v. by jointly training on the recommendation and explanation generation, our model can contextualize the embeddings more adequately with training signals from both tasks. ## model figure 1 illustrates our model with the user-item graph constructed through collaborative filtering signals, an encoder, and inference functions for explanation generation and rating prediction. ## dataset although kg-recommendation datasets exist, they do not contain any supervision signals to nl descriptions. thus, to evaluate our explainable recommendation approach in a kg-aware setting and our knowrec model, we introduce two new datasets based on the amazon-book and amazon-movie datasets @xcite : (1) book kg-exp and (2) movie kg-exp. recall that our task requires an input kg along with an nl explanation and recommendation score. because it is more efficient to extract kgs from text, rather than manually annotate each kg with text, we take a description-first approach, automatically extracting kg elements from the corresponding text. given the currently available data, we leverage item descriptions as a proxy for the nl explanations, while constructing a user-item kg from an item's features and user's purchase history. we first extract entities from a given item description via dbpedia spotlight @xcite , a tool that detects mentions of dbpedia @xcite entities from nl text. we then query for each entity's most specific type and use those types as relations that connect the item to its corresponding entities. we construct a user kg via their purchase history, e.g. [p urchase 1 , p urchase 2 , ...p urchase n ], as a complete graph where each purchase is connected. finally, we connect all the nodes of the user kg to the item kg, treating each user purchase as a one-hop neighbor of the current item. to ensure the kg-explanation correspondence, we filter out any sentences in the explanation in which no entities were found. to measure objectivity, we calculate the proportion of a given kg's entities that appear in the explanation, called entity coverage (ec) (defined in appendix b.2). we summarize our dataset statistics in ## experiments 6.1 evaluation metrics we assess explainable recommendation following prior work: 1) on the recommendation performance and 2) on the explanation performance. for the explanation generation task, we employ standard natural language generation (nlg) metrics: bleu @xcite and rouge @xcite . we measure diversity and the detail-oriented features of the generated sentences using unique sentence ratio (usr) @xcite , and use ec, instead of feature coverage ratio, for coverage due to our non-review-based explanations. ## conclusion we propose knowrec, a knowledge-aware model for generating nl explanations and recommendation scores on user-item pairs. to evaluate knowrec, we devise and release a semi-supervised large-scale kg-nl recommendation dataset in the book and movie domain. extensive experiments on both datasets demonstrate the suitability of our model compared to recently proposed explainable recommendation models. we hope that by proposing this kg-guided task, we will open up avenues to research focused on detailed, objective, and specific explanations which can also scale to new items and users, rather than the current review-focused work. in future work, we plan to incorporate userspecific kgs and other pre-trained language models into our model in order to verbalize both user and item-level feature explanations.