3 research outputs found
MM-GEF: Multi-modal representation meet collaborative filtering
In modern e-commerce, item content features in various modalities offer
accurate yet comprehensive information to recommender systems. The majority of
previous work either focuses on learning effective item representation during
modelling user-item interactions, or exploring item-item relationships by
analysing multi-modal features. Those methods, however, fail to incorporate the
collaborative item-user-item relationships into the multi-modal feature-based
item structure. In this work, we propose a graph-based item structure
enhancement method MM-GEF: Multi-Modal recommendation with Graph Early-Fusion,
which effectively combines the latent item structure underlying multi-modal
contents with the collaborative signals. Instead of processing the content
feature in different modalities separately, we show that the early-fusion of
multi-modal features provides significant improvement. MM-GEF learns refined
item representations by injecting structural information obtained from both
multi-modal and collaborative signals. Through extensive experiments on four
publicly available datasets, we demonstrate systematical improvements of our
method over state-of-the-art multi-modal recommendation methods
Information theory-based compositional distributional semantics
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