Active Learning in Recommender Systems

Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain

doi:10.1007/978-1-4899-7637-6_24

Cited by 157 publications

(144 citation statements)

References 58 publications

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“…In such a case, the system has to obtain a minimum amount of information by explicitly requesting the users to rate a set of items. This typically happens when a new user registers to the system and the system has no or very limited information about this user (new user problem [70]), or when a new item is added to the catalogue and the system has no opinions on that item (new item problem [39]). …”

Section: Solutionmentioning

confidence: 99%

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User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

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Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of Bgood^design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (Brecommendations^) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, Multimed Tools Appl

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Section: Solutionmentioning

confidence: 99%

“…& the system supports different generation techniques, for personalized and non-personalized recommendations; recommendations cannot be offered, but could become available after some interaction steps (new user problem [70]). …”

Section: Usagementioning

confidence: 99%

User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

Self Cite

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“…Moreover, it would be possible to extend our visualization tool with findings from related recommender systems research areas (e.g. active learning [39], critiquing [40], and more extensive explanations).…”

Section: Discussionmentioning

confidence: 99%

Explorative Analysis of Recommendations Through Interactive Visualization

Richthammer

Pernul

2017

Lecture Notes in Business Information Processing

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Abstract. Even though today's recommender algorithms are highly sophisticated, they can hardly take into account the users' situational needs. An obvious way to address this is to initially inquire the users' momentary preferences, but the users' inability to accurately state them upfront may lead to the loss of several good alternatives. Hence, this paper suggests to generate the recommendations without such additional input data from the users and let them interactively explore the recommended items on their own. To support this explorative analysis, a novel visualization tool based on treemaps is developed. The analysis of the prototype demonstrates that the interactive treemap visualization facilitates the users' comprehension of the big picture of available alternatives and the reasoning behind the recommendations. This helps the users get clear about their situational needs, inspect the most relevant recommendations in detail, and finally arrive at informed decisions.

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“…Major challenges in recommender systems are [22] data sparsity, scalability, diversity and vulnerability to attacks. Due to large datasets, the user-item matrix used for filtering could be very large and sparse.…”

Section: Hybrid Filteringmentioning

confidence: 99%

A Framework for Development of Recommender System for Financial Data Analysis

Kanaujia¹,

Pandey²,

Rautaray³

2017

IJIEEB

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Abstract-The huge amount of data is being created every day by various organisations and users all over the world. Structured, semi-structured and unstructured data is being created at a very rapid speed from heterogeneous sources like reviews, ratings, feedbacks, shopping details, etc., it is termed as Big Data. This data generated from different users share many common patterns which can be filtered and analysed to give some recommendation regarding the product, goods or services in which a user is interested. Recommendation systems are the software tools used to give suggestions to users on the basis of their requirements. Today no system is available for suggesting a person on how to use their money for saving, where to invest and how to manage expenditures. Few consulting systems are available which provide investment and saving tips but they are not much effective and are much complex. The presented paper proposed a collaborative filtering based recommender system for financial analysis based on Saving, Expenditure and Investment using Apache Hadoop and Apache Mahout. Many savings and investment consulting systems are available but no system provides effective and efficient recommendation regarding management and beneficial utilisation of salary. The advantage of proposed recommender system is that it provides better suggestion to a person for saving, expenditure and investment of their salary which in turns maximises their wealth. Due to enormous amount of data involved, Apache Hadoop framework is used for distributed processing. Collaborative filtering and Apache Mahout is used for analysing the data and implementation of the recommender system.

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Active Learning in Recommender Systems

Cited by 157 publications

References 58 publications

User interface patterns in recommendation-empowered content intensive multimedia applications

User interface patterns in recommendation-empowered content intensive multimedia applications

Explorative Analysis of Recommendations Through Interactive Visualization

A Framework for Development of Recommender System for Financial Data Analysis

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