Facing the cold start problem in recommender systems

Lika, Blerina; Kolomvatsos, Kostas; Hadjiefthymiades, Stathes

doi:10.1016/j.eswa.2013.09.005

Cited by 476 publications

(222 citation statements)

References 25 publications

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“…These traditional approaches, as they are, cannot be used effectively in mobile environments because of cold-start problem [6][7][8]. The cold-start problem arises when personalization systems lack adequate knowledge of either the new user's preferences or of new items for providing relevant suggestions [6,7]. This often is due to the system's inability to gather sufficient information about users and the items they have preferred in the past [1].…”

Section: Introductionmentioning

confidence: 96%

“…However, some of these systems are designed to maximize business interests rather than user's interests, based on user provided item ratings [4]. These traditional approaches, as they are, cannot be used effectively in mobile environments because of cold-start problem [6][7][8]. The cold-start problem arises when personalization systems lack adequate knowledge of either the new user's preferences or of new items for providing relevant suggestions [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…However, these systems focus essentially on suggesting relevant items from millions of alternatives to users based on the assumption that user's tastes and preferences are fixed [5,12]. They assist target users to explicitly discover relevant items using three popular methods namely: (1) evaluation of previously consumed multimedia content given by users, a process known as content-based filtering (CBF), (2) using evaluation of items consumed by like-minded users, otherwise known as collaborative filtering (CF) [7,26,27] and (3) using a combination of these methods in what is popularly called hybrid recommendations [2,3,23]. Although these conventional personalized recommendation techniques have been highly successful however, they are usually designed to address explicit recommendations of multimedia items.…”

Section: Background and Related Workmentioning

confidence: 99%

“…always provide such rating information for multimedia content they consume, both CF and CBF suffer from the cold-start or new user problem [4,[6][7][8][9]. Therefore, these systems are not always reliable for providing effective recommendations to new users, i.e.…”

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confidence: 99%

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Context-Aware Personalization Using Neighborhood-Based Context Similarity

Otebolaku

Andrade

2016

Wireless Pers Commun

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With the overwhelming volume of online multimedia content and increasing ubiquity of Internet-enabled mobile devices, pervasive use of the Web for content sharing and consumption has become our everyday routines. Consequently, people seeking online access to content of interest are becoming more and more frustrated. Thus, deciding which content to consume among the deluge of available alternatives becomes increasingly difficult. Contextaware personalization, having the capability to predict user's contextual preferences, has been proposed as an effective solution. However, some existing personalized systems, especially those based on collaborative filtering, rely on rating information explicitly obtained from users in consumption contexts. Therefore, these systems suffer from the socalled cold-start problem that occurs as a result of personalization systems' lack of adequate knowledge of either a new user's preferences or of a new item rating information. This happens because these new items and users have not received or provided adequate rating information respectively. In this paper, we present an analysis and design of a context-aware personalized system capable of minimizing new user cold-start problem in a mobile multimedia consumption scenario. The article emphasizes the importance of similarity between contexts of consumption based on the traditional k-nearest neighbor algorithm using Pearson Correlation model. Experimental validation, with respect to quality of personalized recommendations and user satisfaction in both contextual and non-contextual scenarios, shows that the proposed system can mitigate the effect of user-based cold-start problem.

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Context-Aware Personalization Using Neighborhood-Based Context Similarity

Otebolaku

Andrade

2016

Wireless Pers Commun

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“…Lika et al [34] present a technique for solving the cold start problem. Cold start problem arises in collaborative filtering system.…”

Section: B Issues In Recommender Systemsmentioning

confidence: 99%

A Survey on the Generation of Recommender Systems

Singh¹,

chuchra²,

Rani³

2017

IJIEEB

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Abstract-In the era of Internet, web is a giant source of information. The constantly growing rate of information in the web makes people confused to decide which product is relevant to them. To find relevant product in today's era is very time consuming and tedious task. Everyday a lot of information is uploaded and retrieved from the web. The web is overloaded with information and it is very essential to cop up with this overloaded and overlooked information. Recommender systems are the solution which can help a user to get relevant information from the bulk of information. Recommender systems provide customized or personalized and non personalized recommendations to interested users. Recommender systems are in its evolution stage. Recommender systems have been evolved from first generation to third generation through second generation. First generation or Web 1.0 recommender systems deal with E-commerce, Second generation or web 2.0 recommender systems use social network and social contextual information for accurate and diverse recommendations, and Third generation recommender systems use location based information or internet of things for generating recommendations. In this paper, three generation of recommender systems and are discussed. Similarity measures and evaluation metrics are used in these generations are also discussed.

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Efficient Parametric Design‐Space Exploration with Reinforcement Learning‐Based Recommenders

Alam,

Echenagucia

2024

Artificial Intelligence in Performance‐Driven Design

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Facing the cold start problem in recommender systems

Cited by 476 publications

References 25 publications

Context-Aware Personalization Using Neighborhood-Based Context Similarity

Context-Aware Personalization Using Neighborhood-Based Context Similarity

A Survey on the Generation of Recommender Systems

Efficient Parametric Design‐Space Exploration with Reinforcement Learning‐Based Recommenders

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