Enhancing Explainable Rating Prediction through Annotated Macro Concepts (2024)

Abstract: Generating recommendation reasons for recommendation results is a long-standing problem because it is challenging to explain the underlying reasons for recommending an item based on user and item IDs. Existing models usually learn semantic embeddings for each user and item, and generate the reasons according to the embeddings of the user-item pair. However, user and item IDs do not carry inherent semantic meaning, thus the limited number of reviews cannot model users’ preferences and item characteristics effectively, negatively affecting the model generalization for unseen user-item pairs.To tackle the problem, we propose the Concept Enhanced Explainable Recommendation framework (CEER), which utilizes macro concepts as the intermediary to bridge the gap between the user/item embeddings and the recommendation reasons. Specifically, we maximize the information bottleneck to extract macro concepts from user-item reviews. Then, for recommended user-item pairs, we jointly train the concept embeddings with the user and item embeddings, and generate the explanation according to the concepts. Extensive experiments on three datasets verify the superiority of our CEER model.

## introduction in recommender systems, rating prediction is a crucial component for filtering user interest items from a vast pool of candidates. however, many recommender systems only offer rating prediction results without providing any explanations @xcite . personalized and appropriate explanations can greatly arouse user interest and enhance the overall user experience. therefore, there is potential to deliver accurate rating predictions while generating personalized and suitable reasons for recommended items @xcite . ## preliminary problem formulation. we denote the user set and item set as the rating score r ij assigned towards the interaction (u i , v j ) characterizes the degree of user preference. the observed reviews are organized within a matrix x, where each element in the matrix represents a review and x ij represents the review for the rating r ij . x ij is defined as a sentence denoted as [x 1 ..., x m , ..., x m ], where x m is the m th word in x ij . during inference, the available information for each interaction is limited to the user and item id. in this work, we define explanation generation as a sequence-tosequence prediction task. specifically, given a useritem pair, we apply the user and item embeddings as soft prompts, asking the transformer to generate explanations, where each word in the explanation is predicted given the preceding words and soft prompt. for example, given the soft prompt [u i , v j ], the transformer is trained to predict the next word x 1 . after multiple generation steps, the output from the transformer forms the sequence [x 1 ..., x m , ..., x m ]. the notations are put into appendix a.1 ## methodology this section introduces two primary components of our framework: a macro concept annotator and an explanation generator. the first component involves two steps: identifying the informative micro characteristics of items in the reviews and using llms to annotate macro concepts. the second component utilizes three tasks based on the annotated concepts to enhance user and item embeddings and facilitate explanation generation. the architecture sketch is provided in figure 1 . ## experiments in this section, we perform comprehensive experiments to validate the effectiveness of ceer and gain insights into its behavior. we aim to answer the following research questions: rq1: how well do the explanations and ratings generated by ceer and baselines align with the actual labels in terms of text quality? rq2: how does the quality of explanations generated by all models in terms of interpretability? rq3: how do different tasks contribute to the whole model performance? rq4: what impact do different hyper-parameter settings of the proposed tasks have on ceer? rq5: how does information bottleneck perform compared with other micro characteristics identification methods for macro concept annotation? ## conclusion and future work in this paper, we present the ceer framework, designed to enrich user and item embeddings with high-level semantics, i.e., macro concepts to improve the explanation generation. the framework identifies micro characteristics of items from associated reviews and utilizes llms to annotate macro concepts. these macro concepts act as intermediaries to align the user and item embeddings with review embeddings to obtain more semantic information. and we achieve this through three tasks that focus on establishing relationships between users/items, macro concepts, and reviews. we conduct extensive experiments, and the results confirm the effectiveness of ceer. our future work will involve adapting our method to annotate concepts from various data sources, such as images, and enhancing the explanation generation process @xcite .