Recursive Neural Language Architecture for Tag Prediction
This work addresses tag recommendation for users in systems with sparse tagging data, presenting an incremental improvement over existing neural methods.
The paper tackles the problem of learning distributed representations for tags from sparse content-tag associations to improve tag recommendation, achieving significant improvements in recommendation quality over baselines on two datasets.
We consider the problem of learning distributed representations for tags from their associated content for the task of tag recommendation. Considering tagging information is usually very sparse, effective learning from content and tag association is very crucial and challenging task. Recently, various neural representation learning models such as WSABIE and its variants show promising performance, mainly due to compact feature representations learned in a semantic space. However, their capacity is limited by a linear compositional approach for representing tags as sum of equal parts and hurt their performance. In this work, we propose a neural feedback relevance model for learning tag representations with weighted feature representations. Our experiments on two widely used datasets show significant improvement for quality of recommendations over various baselines.