Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications. Research papers on various recommender system applications are summarized.The recommender systems are examined systematically through four dimensions.The recommender system applications are classified into eight categories.Related recommendation techniques in each category are identified.Several new recommendation techniques and application areas are uncovered.

Recommender system application developments

Journal of the ACM

Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model support d by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world. Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks

Exploratory Search:Beyond the Query-Response Paradigm

In this paper we focus on one particular implementation of social search, namely HeyStaks, which combines ideas from web search, content curation, and social networking to make recommendations to users, at search time, based on topics that matter to them. The central concept in HeyStaks is the search stak. Users can create and share staks as a way to curate their search experiences. A key problem for HeyStaks is the need for users to pre-select their active stak at search time, to provide a context for their current search experience so that HeyStaks can index and store what they find. The focus of this paper is to look at how machine learning techniques can be used to recommend a suitable active stak to the user at search time automatically.

A Comparison of Machine Learning Techniques for Recommending Search Experiences in Social Search

In this paper we describe an experiment that applies collaborative filtering techniques to a Web search task, namely the prediction of the rank of relevant results for a given query. We compare the performance of two different collaborative filtering algorithms and argue that these approaches have the potential to generate accurate predictions, which in turn may help to improve existing Web search techniques.

On the use of collaborative filtering techniques for the prediction of Web search result rank

Paper presented at the 22nd Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2011), University of Ulster, Northern Ireland, 31 August - 2 September, 2011

/pdf/recommending-search-experiences-bqe35catlz.pdf

Recommending search experiences

Presented at the 1st International Workshop on Recommendation-Based Industry Applications at The 3rd ACM Conference on Recommender Systems (RecSys 2009), October 2009, New York, NY, USA

/pdf/demonstrating-social-search-a-la-heystaks-10trzf7siz.pdf

Demonstrating social search a la HeyStaks

Beyond Search: Computational Intelligence for the Web Workshop at the twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)

/pdf/trust-enhanced-peer-to-peer-collaborative-web-search-2m53a3es2c.pdf

Peter Briggs

Papers

A Comparison of Machine Learning Techniques for Recommending Search Experiences in Social Search

On the use of collaborative filtering techniques for the prediction of Web search result rank

Recommending search experiences

Demonstrating social search a la HeyStaks

Trust-enhanced peer-to-peer collaborative web search