Recommender systems based on collaborative filtering and resource allocation

Javari, Amin; Gharibshah, Joobin; Jalili, Mahdi

doi:10.1007/s13278-014-0234-0

Cited by 32 publications

(15 citation statements)

References 39 publications

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“…Fig. 3 shows the MAE of CUPCF in comparison with a number of state-of-the-art recommendation methods mentioned in references of [18,[28][29]. The percentage of improving of CUPCF than other models and techniques such as CF is 2.5%, CF-RA is 0.28%, CF-Diff is 0.8%, CF-Rank is 1.2%, CF-MW is 1.1%, CF-HW is 1.9%, Pearson is 1.5%, RA-COS is 1.4%, RA-SRC is 5.7%, SRC is 11.4%, RA-CPC is 1.1%, CPC is 2.3%, Three-Segment is 2.5%, BCF is 6.3%, NHSM is 12%, PIP is 15%, COS is 15.5%, K-Means Leader is 1.2% and K-Means is 3.2%.…”

Section: Resultsmentioning

confidence: 99%

“…Javari et al proposed a recommender system based on collaborative filtering and resources allocation for improving the performance of their system. Using the resource allocation method, they were able to obtain the degree of confidence of each user based on the similarity achieved [18]. In 2017, Khalaji designed a hybrid recommender system based on neural network and resource allocation that solved the scalability and cold start problems [19].…”

Section: Related Workmentioning

confidence: 99%

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CUPCF: combining users preferences in collaborative filtering for better recommendation

Khalaji

Mohammadnejad

2019

SN Appl. Sci.

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How to make the best decision between the opinions and tastes of your friends and acquaintances? Therefore, recommender systems are used to solve such issues. The common algorithms use a similarity measure to predict active users' tastes over a particular item. According to the cold start and data sparsity problems, these systems cannot predict and suggest particular items to users. In this paper, we introduce a new recommender system is able to find user preferences and based on it, provides the recommendations. Our proposed system called CUPCF is a combination of two similarity measures in collaborative filtering to solve the data sparsity problem and poor prediction (high prediction error rate) problems for better recommendation. The experimental results based on MovieLens dataset show that, combined with the preferences of the user's nearest neighbor, the proposed system error rate compared to a number of state-of-the-art recommendation methods improved. Furthermore, the results indicate the efficiency of CUPCF. The maximum improved error rate of the system is 15.5% and the maximum values of Accuracy, Precision and Recall of CUPCF are 0.91402, 0.91436 and 0.9974 respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

CUPCF: combining users preferences in collaborative filtering for better recommendation

Khalaji

Mohammadnejad

2019

SN Appl. Sci.

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“…On the other hand, service prediction works proactively propose services based on user's historical context information. Similar to traditional recommender systems, such as Javari et al (2014), that consider the correlation among user and items, service prediction works, such as Adomavicius and Tuzhilin (2011), Xiao et al (2010) and Baltrunas and Ricci (2013), consider the correlation between context information and an item (e.g. a service) using different filtering techniques (Baltrunas and Ricci 2013), which can be correlated with ontology-based matching (Xiao et al 2010).…”

Section: Related Workmentioning

confidence: 99%

Service discovery and prediction on Pervasive Information System

Najar

Pinheiro

Souveyet

2015

J Ambient Intell Human Comput

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International audienceRecent evolution of technology and its usages, such as BYOD (Bring Your Own Device) and IoT (Internet of Things), transformed the way we interact with Information Systems (IS), leading to a new generation of IS, called the Pervasive Information Systems (PIS). These systems have to face heterogeneous pervasive environments and hide the complexity of such environment end-user. In order to reach transparency and proactivity necessary for successful PIS, new discovery and prediction mechanisms are necessary. In this paper, we present a new user-centric approach for PIS and propose new service discovery and prediction based on both user's context and intentions. Intentions allow focusing on goals user wants to satisfy when requesting a service. Those intentions rise in a given context, which influence the service implementation. We propose a service discovery mechanism that observes user's context and intention in order to offer him/her the most appropriate service satisfying her/his intention on the current context. We also propose a prediction mechanism that tries to anticipate user's intentions considering the user's history and the observed context. We evaluate both mechanisms and discuss advanced features future PIS will have to deal with

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“…neighbor users) are used to produce suitable recommendations to a given user (i.e. an active user) in the recommendation process [1,[8][9][10][11][12][13][14][15][16]. The CF approach uses rates of the active user to previously purchased items to make recommendations.…”

Section: Introductionmentioning

confidence: 99%