Recommendation to Groups of Users Using the Singularities Concept

Ortega, Fernando; Hurtado, Remigio; Bobadilla, Jesús; Bojorque, Rodolfo

doi:10.1109/access.2018.2853107

Cited by 13 publications

(15 citation statements)

References 57 publications

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“…Once the above three blocks (CF RS, clustering and MFbased clustering) have been addressed we will focus on the CF recommendations to groups of users. The recommendations to groups of users arise from the convenience of being able to recommend a group of users about products or services that satisfy the entire group [45].…”

Section: Recommendation To Groups Of Users and Proposed Approachmentioning

confidence: 99%

“…Once the virtual user is obtained, the traditional recommendation to one user process is done; this approach is the more accurate one when applied to heterogeneous groups. We call it VUR (Virtual User based Recommendation) [45], [51], [55] and we use it as a baseline. Method d) [56], [52] makes predictions before clustering, it performs aggregation post-clustering and it does not use dimensionality reduction.…”

Section: Recommendation To Groups Of Users and Proposed Approachmentioning

confidence: 99%

“…1, from methods a) to d) we show the most relevant researches in the context of the proposed approach (designed to make recommendations to groups of users). This works are: a) Recommendation merging [49], b) Prediction aggregation [54], c) User preferences aggregation [45], [51], [55], and, d) Predict & Cluster [56], [52]. In addition to these methods, there are other models and methods based on matrix factorization, neural collaborative filtering, clustering methods, and graph approaches.…”

Section: Related Workmentioning

confidence: 99%

“…To measure the performance of the proposed model we will compare it with state-of-the-art group recommendation methods. The baselines selected for this comparison are PC [52], [56], RAP [57], and VUR [45], [51], [55]. Some of these recommendation methods require different parameters to work.…”

Section: Model Performancementioning

confidence: 99%

See 3 more Smart Citations

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

Bobadilla¹,

Gutiérrez²,

Alonso³

et al. 2020

IJIMAI

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In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups.

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Section: Recommendation To Groups Of Users and Proposed Approachmentioning

confidence: 99%

Section: Recommendation To Groups Of Users and Proposed Approachmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Model Performancementioning

confidence: 99%

See 2 more Smart Citations

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

Bobadilla¹,

Gutiérrez²,

Alonso³

et al. 2020

IJIMAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [9], authors use the common singularity ratings provided by users to determine the similarity between the users. Author's uses lower probability to choose a neighbor that best fit the preferences of a group.…”

Section: Related Workmentioning

confidence: 99%

Exploration of Collaboration Filtering Techniques for Product Recommendation

Kumar,

Madhavan

2020

IJEAT

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Today, recommendation system has been globally adopted as the most effective and reliable search engine for knowledge extraction in the field of education, economics and scientific research. Collaborative filtering is a proven techniques used in recommender system to make predictions or recommendations of the unknown preferences for users based on the known user preferences. In this paper, collaborative filtering task and their challenges are explored, study the different recommendation techniques and evaluate their performance using different metrics.

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