id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020885-f667icyt	Sharma, Ujjwal	Semantic Path-Based Learning for Review Volume Prediction	2020-03-17		.txt	text/plain	4026	245	48	In this work, we present an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes. In this work, -We propose a novel method that incorporates restaurants and their attributes into a multimodal graph and extracts multiple, bimodal low dimensional representations for restaurants based on available paths through shared visual, textual, geographical and categorical features. In this section, we discuss prior work that leverages graph-based structures for extracting information from multiple modalities, focussing on the auto-captioning task that introduced such methods. For each of these sub-networks, we perform random walks and use a variant of the heterogeneous skip-gram objective introduced in [6] to generate low-dimensional bimodal embeddings. Our attention-based model combines separately learned bimodal embeddings using a late-fusion setup for predicting the review volume of the restaurants.	./cache/cord-020885-f667icyt.txt	./txt/cord-020885-f667icyt.txt