Item-based collaborative filtering recommendation algorithms

Sarwar, Badrul; Karypis, George; Konstan, Joseph A.; Riedl, John

doi:10.1145/371920.372071

Cited by 7,122 publications

(4,256 citation statements)

References 16 publications

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“…We have chosen this algorithm because it is widely used to recommend items when the modeling of user preferences is not a valid option (as in most of real scenarios [17][18][19] and others [20][21][22][23]). This algorithm requires users to rate an initial set of items.…”

Section: Group Recommendation Systems Grsmentioning

confidence: 99%

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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With the development of social networks and online mobile communities, group recommendation systems support users' interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users' personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external socialtrust networks is proposed, which builds a group profile by analyzing not only users' preferences, but also the social relationships between members inside and outside of the group. We employ the users' degree of disagreement to adjust group preference rating by external information of social-trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.

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Section: Group Recommendation Systems Grsmentioning

confidence: 99%

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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“…Consequently, they suffer from the sparsity problem if rating/selection history is short. CF algorithms can be further divided into two categories: memory-based CF [23], Item-KNN [28] and model-based CF [30]. The hybrid approaches combine the content-based and CF methods to avoid their limitations.…”

Section: Related Workmentioning

confidence: 99%

“…Maybe not. To cope with the problem, most existing approaches [14], [15], [23], [28] have been proposed to recommend the users personal significant and interesting items on e-commence websites, by estimating unknown rating which the user may rate the unrated item, i.e., rating prediction as (user,rating=?,item). Basically, the more high rating the user may rate the item, the more possible that the item would be recommended to the user.…”

Section: Introductionmentioning

confidence: 99%

A Novel Embedding Model for Relation Prediction in Recommendation Systems

Gao

Gallinari

Guo

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIt inevitably comes out information overload problem with the increasing available data on e-commence websites. Most existing approaches have been proposed to recommend the users personal significant and interesting items on e-commence websites, by estimating unknown rating which the user may rate the unrated item, i.e., rating prediction. However, the existing approaches are unable to perform user prediction and item prediction, since they just treat the ratings as real numbers and learn nothing about the ratings' embeddings in the training process. In this paper, motivated by relation prediction in multi-relational graph, we propose a novel embedding model, namely RPEM, to solve the problem including the tasks of rating prediction, user prediction and item prediction simultaneously for recommendation systems, by learning the latent semantic representation of the users, items and ratings. In addition, we apply the proposed model to cross-domain recommendation, which is able to realize recommendation generation in multiple domains. Empirical comparison on several real datasets validates the effectiveness of the proposed model. The data is available at https://github.com/yuzhaour/da.

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“…Concerning the comparability of clients, [262] and [263] state that for the most part either a relationship based likeness (e.g., Pearson connection coefficient) or a cosinesimilitude measure in view of client profile vectors is connected. With respect to thing based sifting, the objective is to locate the most comparable things taking into account the client profiles of the clients who appraised these things [264]. The most comparable things are positioned and in this way given to the client.…”

Section: Memory-based Cfmentioning

confidence: 99%