Infusing Collaborative Recommenders with Distributed Representations Greg Zanotti1, Miller Horvath2, Lucas Nunes Barbosa2 ∗ Venkata Trinadh Kumar Gupta Immedisetty1, Jonathan Gemmell1 1Center for Web Intelligence School of Computing, DePaul University Chicago, IL, USA 2CAPES Foundation Ministry of Education of Brazil Brasilia, DF, BRA gzanotti,millerhorvath,lnbarbosa,vimmedis,
[email protected] ABSTRACT Keywords Recommender systems assist users in navigating complex Collaborative filtering; neural networks; recommender sys- information spaces and focus their attention on the content tems; distributed representations most relevant to their needs. Often these systems rely on user activity or descriptions of the content. Social annota- tion systems, in which users collaboratively assign tags to 1. INTRODUCTION items, provide another means to capture information about Modern websites provide a myriad of conveniences. Users users and items. Each of these data sources provides unique can browse news articles, watch movies, download books benefits, capturing different relationships. or post their latest vacation pictures. The sheer volume In this paper, we propose leveraging multiple sources of of available content can be overwhelming and users often data: ratings data as users report their affinity toward an suffer from information overload. Consequently, websites item, tagging data as users assign annotations to items, and often offer some level of personalization. item data collected from an online database. Taken to- By personalizing a user's interaction with the website, the gether, these datasets provide the opportunity to learn rich user's attention can be focused on the content most related distributed representations by exploiting recent advances in his or her interests. Personalization might be achieved by neural network architectures.