A Multi-Modality Deep Network for Cold-Start Recommendation

Abstract

Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with few ratings. Hybrid recommender systems that combine collaborative filtering and content-based approaches have been proved as an effective way to alleviate the cold-start issue. Integrating contents from multiple heterogeneous data sources such as reviews and product images is challenging for two reasons. Firstly, mapping contents in different modalities from the original feature space to a joint lower-dimensional space is difficult since they have intrinsically different characteristics and statistical properties, such as sparse texts and dense images. Secondly, most algorithms only use content features as the prior knowledge to improve the estimation of user and item profiles but the ratings do not directly provide feedback to guide feature extraction. To tackle these challenges, we propose a tightly-coupled deep network model for fusing heterogeneous modalities, to avoid tedious feature extraction in specific domains, and to enable two-way information propagation from both content and rating information. Experiments on large-scale Amazon product data in book and movie domains demonstrate the effectiveness of the proposed model for cold-start recommendation.

A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification

“…DEGARI by definition falls into the latter category since in its current form it uses content description (obtained in different ways) as the input. From the technical point of view, however, it differs from the current mainstream approaches that are mostly based on the comparison and matching of visual and perceptual features of the content [51,11]. In practice, our approach adds a logic layer capable of mapping and representing -in a commonsense and cognitively compliant fashion -new emotional categories which can be used to affect user preferences and content consumption in a way that cannot be derived from the pure statistical analysis of content and/or the comparison of similar users.…”

Deep Learning-Based Recommendation System: Systematic Review and Classification

“…Frequently used in recommendation systems for processing visual information associated with items [132].…”