Faithfully Explainable Recommendation via Neural Logic Reasoning (2021)

Abstract: Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of the derived explanations to justify the decision-making process. To the best of our knowledge, this is the first work that models and evaluates faithfully explainable recommendation under the framework of KG reasoning. Specifically, we propose neural logic reasoning for explainable recommendation (LOGER) by drawing on interpretable logical rules to guide the path-reasoning process for explanation generation. We experiment on three large-scale datasets in the e-commerce domain, demonstrating the effectiveness of our method in delivering high-quality recommendations as well as ascertaining the faithfulness of the derived explanation.

## introduction compared with traditional recommender systems (rs), explainable recommendation is not only capable of providing high-quality recommendation results but also offers personalized and intuitive explanations @xcite . incorporating a knowledge graph (kg) into recommender systems has become increasingly popular, since kg reasoning is able to generate explainable paths connecting users to relevant target item entities. at the same time, there is increasing demand for systems to ascertain the faithfulness of the generated explanation, i.e., assess whether it faithfully reflects the reasoning process of the model and is consistent with the historic user behavior. however, previous work has largely neglected faithfulness in kg-enhanced explainable recommendation @xcite . a number of studies @xcite @xcite argue that * equal contribution faithful explanations should also be personalized and gain the capability to reflect the personalized user historic behavior. however, to the best of our knowledge, none of the existing explainable recommendation models based on kgs have considered faithfulness in the explainable reasoning process and its evaluation on the generated explainable paths. for instance, pgpr @xcite infers explainable paths over the kg without considering personalized user behavior, and its prediction on next potential entities is merely based on the overall knowledge-driven rewards. cafe @xcite builds user module profiles to guide the path inference procedure. however, as illustrated in @xcite , such neural module networks only implicitly abstract the reasoning process and lack of considering the faithfulness of explanations. in this paper, we propose a new kg-enhanced recommendation model called loger to produce faithfully explainable recommendation via neural logic reasoning. to fully account for heterogeneous information and rules about users and items from the kg, we leverage an interpretable neural logic model for logical reasoning, enhanced by a general graph encoder that learns kg representations to capture semantic aspects of entities and relations. these two components are iteratively trained via the em algorithm by marrying the merits of interpretability of logical rules and the expressiveness of kg embeddings. subsequently, the learned rule weights are leveraged to guide the path reasoning to generate faithful explanations. the derived logical rules are expected to be consistent with historic user behavior and the resulting paths genuinely reflect the decision making process in kg reasoning. we experiment on three large-scale datasets for e-commerce recommendation that cover rich user behavior patterns. the results demonstrate the superior recommendation performance achieved by our model compared to the state-of-the-art base-lines, with the guarantee of the faithfulness on the generated path-based explanations. the contributions of this paper are threefold. ## problem formulation a knowledge graph (kg) for recommendation is defined as @xmath0, r, e t ) | e h , e @xmath1, @xmath2, where e denotes the entity set consisting of sets of users u, items i, and other entities, while r denotes the relation set. each triplet (e h , r, e t ) represents a fact indicating head entity e h interacts with tail entity e t via relation r. in recommendation tasks, we are particularly interested in user-item interactions {(u, r ui , v) | @xmath3, r @xmath4, @xmath5. the problem of kg reasoning for explainable recommendation is formulated as follows. given an incomplete kg g with missing user-item interactions, for every user @xmath6, the goal is to select a set of items as recommendations {v|(u, r ui , v) ∈ g, @xmath7. the key challenge is to not only guarantee the recommendation quality with the rich information in kg, but also generate faithful explanations that reflect the actual decision-making process of the recommendation model and are consistent with historic user behavior. ## proposed method we introduce the novel neural logic explainable recommender (loger) for producing faithfully explainable recommendations with a kg. as illustrated in fig. 1 , it consists of three components: (i) a kg encoder for learning embeddings of kg entities and relations to capture their semantics, (ii) a neural logic model for conducting interpretable logical reasoning to make recommendations, and (iii) a rule-guided path reasoner for generating faithfully explainable paths. both kg encoder and neural logic model are trained iteratively via the em algorithm (neal and hinton, 1998) so that they mutually benefit to make recommendations via logical reasoning. additionally, personalized rule importance scores are derived for every user and leveraged to guide the path reasoning for faithful explanation generation. ## experiment dataset we experiment on three domain-specific e-commerce datasets from amazon, namely cellphones, grocery, and automotive. there are two requirements that lead to the selection of these categories in our experiments. first, the constructed kg should contain rich user behavior patterns, e.g., user mentioned features or preferred styles, etc. this is the major difference from most of the existing work @xcite , which only extends knowledge on the item side. second, the kgs are assumed to be large-scale. we select several large subsets from @xcite , where the constructed kg can be regarded as an updated version of those of @xcite based on the amazon review dataset @xcite . the remaining three datasets are the ones that satisfy both of the aforementioned requirements. statistical details of datasets are provided in the appendix. baselines & metrics we consider several stateof-art baselines in the following experiments. cke @xcite uses semantic representations derived from transr @xcite to enhance the matrix factorization process. rip-plenet @xcite is a hybrid method combining regularization and path formats, and augmenting user representations with a memorynetwork-like approach. pgpr @xcite designed a policy-guided graph search algorithm for recommendation over kgs. heteroembed @xcite aims to learn the embeddings of a heterogeneous graph including users, items, and relations for recommendation. kgat @xcite explicitly models higher-order kg connectivity and learns node representations by propagating the embedding of neighbors with corresponding importance discriminated by an attention mechanism. we adopted the same metrics as @xcite to evaluate the recommendation performance of all models: precision, recall, normalized discounted cumulative gain (ndcg), and hit rate (hr). ## conclusion in this paper, we propose loger for faithfully explainable recommendation, which generates explainable paths based on personalized rule importance scores via neural logic reasoning that adequately captures historic user behavior. we experiment on three large-scale datasets for e-commerce recommendation showing superior recommendation quality of loger as well as the faithfulness of the generated explanations both quantitatively and qualitatively. we hope to encourage future work that values explainability and in particular the faithfulness of explanations. our code is available at https://github.com/orcax/loger .