On context- and sequence-aware document enrichment and retrieval towards personalized recommendations

The amount of unstructured data has grown exponentially during the past two decades and continues to grow at even faster rates. As a consequence, the efficient management of this kind of data came to play an important role in almost all organizations. Up to now, approaches from many different research fields, like information search and retrieval, text mining or query expansion and reformulation, have enabled us to extract and learn patterns in order to improve the management, retrieval and recommendation of documents. However, there are still many open questions, limitations and vulnerabilities that need to be addressed. This paper aims at identifying the current major challenges and research gaps in the field of document enrichment, retrieval and recommendation, introduces innovative ideas towards overcoming these limitations and weaknesses, and shows the benefits of adopting these ideas into real enterprise content management systems. © Springer International Publishing Switzerland 2014.