Addressing cold-start problem in recommendation systems

Lam, Xuan Nhat; Vu, Thuc; Le, Trong Duc; Duong, Anh Duc

doi:10.1145/1352793.1352837

Cited by 291 publications

(149 citation statements)

References 7 publications

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“…The new community problem (Schein, Popescul, Ungar, & Pennock, 2002;Lam, Vu, Le, & Duong, 2008) refers to the difficulty, when starting up a RS, in obtaining, a sufficient amount of data (ratings) for making reliable recommendations. Two common ways are used for tackling this problem: to encourage users to make ratings through different means (e.g.…”

Section: The Cold Start Problemmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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Proporcionaré una formalización completa del método propuesto. Explicaré cómo obtener el conjunto de k vecinos del grupo de usuarios y mostraré cómo obtener recomendaciones usando dichos vecinos. Asimismo, incluiré un ejemplo detallando cada paso del método propuesto en un sistema de recomendación compuesto por 8 usuarios y 10 items.Las principales características del método propuesto son: (a) es más rápido (más eficiente) que las alternativas proporcionadas por otros autores, y (b) es al menos tan exacto y preciso como otras soluciones estudiadas. Para contrastar esta hipótesis realizaré varios experimentos que miden la precisión, la exactitud y el rendimiento del método. Los resultados obtenidos se compararán con los resultados de otras alternativas utilizadas en la recomendación de grupos. Los experimentos se realizarán con las bases de datos de MovieLens y Netflix. VIII IX ABSTRACTThe importance of recommender systems has grown exponentially with the advent of social networks. In this PhD thesis I will provide a wide vision about the state of the art of recommender systems. They were initially based on demographic, contentbased and collaborative filtering. Currently, these systems incorporate some social information to the recommendation process. In the future, they will use implicit, local and personal information from the Internet of Things.As we will see here, recommender systems based on collaborative filtering can be used to perform recommendations to group of users. Previous works have made this modification in different stages of the collaborative filtering algorithm: establishing the neighborhood, prediction phase and determination of recommended items. In thisPhD thesis I will provide a new method that carry out the unification process (many users to one group) in the first stage of the collaborative filtering algorithm: similarity metric computation.I will provide a full formalization of the proposed method. I will explain how to obtain the k nearest neighbors of the group of users and I will show how to get recommendations using those users. I will also include a running example of a recommender system with 8 users and 10 items detailing all the steps of the method I will present.The main highlights of the proposed method are: (a) it will be faster (more efficient) that the alternatives provided by other authors, and (b) it will be at least as precise and accurate as other studied solutions. To check this hypothesis I will conduct several experiments measuring the accuracy, the precision and the performance of my method. I will compare these results with the results generated by other methods of group recommendation. The experiments will be carried out using MovieLens and Netflix datasets. The kinds of filtering most used at the beginning of the RS (collaborative, content-based and demographic) were described in Pazzani (1999). Breese,

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Section: The Cold Start Problemmentioning

confidence: 99%

Incorporating group recommendations to recommender systems: Alternatives and performance

Ortega

Bobadilla

Hernando

et al. 2013

Information Processing & Management

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“…Data sparsity mainly cause by those two reasons, the first is when users mark the project they also pay the price, such as privacy leaks and time waste; the second is the rate of growth is often faster than the speed of user experience. The cold start problem has two main forms: cold start of new project and cold start of new user [3] . Data sparsity and cold start problem are the important reasons that lead to loss of recommendation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Collaborative Filtering Recommendation Algorithm Based on User Attributes

Su¹,

Li²,

Li³

2017

dtcse

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Abstract. The rapid development of the Internet brings the convenience but also results in "information trek" problem, which is solved by personalized recommendation algorithms. Traditional collaborative filtering algorithms face data sparsity problem. In order to solve those problems, this paper proposes a dynamic adaptive collaborative filtering recommendation algorithm based on user attributes. The algorithm first uses the item properties to build the user preference matrix, and then establishes the user rating preference matrix according to different behavior habits of users. Finally, based on these two matrixes, it prefills and dynamically adjusts the rating matrix. Experimental results show that the algorithm is more accurate in terms of the recommendation accuracy compared with traditional algorithms.

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“…In addition, for new network learners, there is no information about their learning habits and the resources selection history is not available. In the above two situations, if the information is used to recommend learning resources, the cold start problem will be confronted [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Learning Forum Posts Topic Discovery and Its Application in Recommendation System

Luo¹,

Zhang³

et al. 2015

JSW

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Abstract:What a network learner post on a network learning forum directly reflects the learners' need during from the network learning process. The network learning supporting service could be greatly improved with mining topics from the forum posts. For this purpose, this paper employs the Learner-Topic (LT) model to mine learners' posts and discover the topics. The results from the model are used to search the learners who have the same learning interest and recommend the learning resources in a recommendation system.

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Addressing cold-start problem in recommendation systems

Cited by 291 publications

References 7 publications

Incorporating group recommendations to recommender systems: Alternatives and performance

Incorporating group recommendations to recommender systems: Alternatives and performance

Adaptive Collaborative Filtering Recommendation Algorithm Based on User Attributes

Learning Forum Posts Topic Discovery and Its Application in Recommendation System

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