Self-explaining recommenders enhance user trust by providing justifications for their suggestions. Sequence-aware models have advanced the field by leveraging user interaction history to personalize recommendations and explanations. However, generative models often struggle with sparse data, producing repetitive or irrelevant explanations. This paper explores the optimal methods for infusing rich textual information from past user interactions directly into the item embeddings to feed a user reasoning path leading to personalized explanations. We conduct a comprehensive analysis of various techniques, including: (1) multiple text aggregation strategies to pool fine-grained attributed item opinions into user-aggregated item text representations; (2) several fusion mechanisms to combine text and collaborative modalities, from early fusion to a late fusion approach within the Transformer architecture; and (3) different training regimes for explanation generation. Experiments on three real-world datasets demonstrate which steps to follow in order to successfully leverage textual information into a sequence-aware explainable recommendation model and boost recommendation performance as well as explanation quality.

One way to increase trust among users towards recommender systems is to provide the recommendation along with a textual explanation. In the literature, extraction-based, generation-based, and, more recently, hybrid solutions based on retrieval-augmented generation have been proposed to tackle the problem of text-based explainable recommendation. However, the use of different datasets, preprocessing steps, target explanations, baselines, and evaluation metrics complicates the reproducibility and state-of-the-art assessment of previous work among different model categories for successful advancements in the field. Our aim is to provide a comprehensive analysis of text-based explainable recommender systems by setting up a well-defined benchmark that accommodates generation-based, extraction-based, and hybrid approaches. Also, we enrich the existing evaluation of explainability and text quality of the explanations with a novel definition of feature hallucination. Our experiments on three real-world datasets unveil hidden behaviors and confirm several claims about model patterns. Our source code and preprocessed datasets are available at https://github.com/alarca94/text-exp-recsys24.

Self-explaining models are becoming an important perk of recommender systems, as they help users understand the reason behind certain recommendations, which encourages them to interact more often with the platform. In order to personalize recommendations, modern approaches make the model aware of the user behavior history for interest evolution representation. However, existing explainable recommender systems do not consider the past user history to further personalize the explanation based on the user interest fluctuation. In this work, we propose a SEQuence-Aware Explainable Recommendation model (SEQUER) that is able to leverage the sequence of user-item review interactions to generate better explanations while maintaining recommendation performance. Experiments validate the effectiveness of our proposal on multiple recommendation scenarios. Our source code and preprocessed datasets are available at https://github.com/alarca94/sequer-recsys23.

Abstract In the context of text representation, Compositional Distributional Semantics models aim to fuse the Distributional Hypothesis and the Principle of Compositionality. Text embedding is based on co-ocurrence distributions and the representations are in turn combined by compositional functions taking into account the text structure. However, the theoretical basis of compositional functions is still an open issue. In this article we define and study the notion of Information Theory–based Compositional Distributional Semantics (ICDS): (i) We first establish formal properties for embedding, composition, and similarity functions based on Shannon’s Information Theory; (ii) we analyze the existing approaches under this prism, checking whether or not they comply with the established desirable properties; (iii) we propose two parameterizable composition and similarity functions that generalize traditional approaches while fulfilling the formal properties; and finally (iv) we perform an empirical study on several textual similarity datasets that include sentences with a high and low lexical overlap, and on the similarity between words and their description. Our theoretical analysis and empirical results show that fulfilling formal properties affects positively the accuracy of text representation models in terms of correspondence (isometry) between the embedding and meaning spaces.