Personalized Group Recommendation Model Based on Argumentation Topic

Chen, Xiong; Lv, Ke; Wang, Hao; Qi, Caiyun

doi:10.1007/978-3-319-93659-8_18

Cited by 1 publication

(1 citation statement)

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“…Our paper borrows this concept to design the proposed approach. A model based on the topic of argumentation [58] is used to recommend personalized items for groups. The argumentation subject is extracted and the users with similar views are clustered into groups.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%