On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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Question Answering (QA) targets answering questions defined in natural language. Question Answering Systems offer an automated approach to procuring solutions to queries expressed in natural language. A lot of QA surveys have classified QuestionAnswering systems based on different criteria such as queries inquired by users, features of data bases used, nature of generated answers, question answering approaches and techniques. To fully understand QA systems, how it has grown into its current QA needs, and the need to scale up to meet future expectations, a broader survey of QA systems becomes essential. Hence, in this paper, we take a short study of the generic QA framework vis a vis Question Analysis, Passage Retrieval and Answer Extraction and some important issues associated with QA systems. These issues include Question Processing, Question Classes, Data Sources for QA, Context and QA, Answer Extraction, Real time Question Answering, Answer Formulation, Multilingual (or cross-lingual) question answering, Advanced reasoning for QA, Interactive QA, User profiling for QA and Information clustering for QA. Finally, we classify QA systems based on some identified criteria in literature. These include Application domain, Question type, Data source, Form of answer generated, Language paradigm and Approaches. We subsequently made an informed judgment of the basis for each classification criterion through literature on QA systems.

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