Special Issue "Recommendation, Information Retrieval, and Exploratory Search"
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (30 September 2021).
Recommendation, information retrieval, and exploratory search are prominent, interrelated tasks in intelligent information access.
Recommender systems perform information filtering and predict ratings or preferences of users among different content items, based on incomplete information on content features and user preferences, with multiple approaches such as collaborative and content-based filtering approaches and reinforcement learning approaches. Information retrieval, in turn, aims at retrieving and ranking content based on its relevance to a user's information need, often expressed by a query and its refinements through filtering and relevance feedback, ranging from general web search to product search and other domain-specific search settings. Lastly, exploratory search can be seen as an information retrieval setting where the user must not only retrieve relevant results but also become familiar with a body of knowledge to complete more complex tasks, so that the specific search intent may evolve as the task goes on.
For each of these general tasks, several models of content, including language models for text and content models of other data types, models of user intent, scoring methods, interaction paradigms and interfaces, and evaluation methodologies, have been designed. These are based on a variety of statistical and machine learning approaches, as well as several approaches from information visualization, visual analytics and human–technology interaction for interactive systems. In this Special Issue, we are seeking articles that represent the state of the art and novel approaches in these different tasks. We are especially interested in articles in the intersection of these tasks: targeting a combination of their settings and learning goals, or bridging methodological approaches across the tasks.
Prof. Dr. Jaakko Peltonen
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- information retrieval
- recommendation system
- exploratory search
- intelligent information access
- machine learning
- visual analytics