Applying landmarks to enhance memory-based collaborative filtering

Lima, Gustavo R.; Mello, Carlos E.; Lyra, Adria; Zimbrão, Geraldo

doi:10.1016/j.ins.2019.10.041

Cited by 36 publications

(9 citation statements)

References 31 publications

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“…It can be seen that the chosen preference similarity measure determines the accuracy of memory-based collaborative filtering. Some recent preference similarity measures for high accuracy are OS [14] and LM [34]. OS [14] combines Percentage of Non-Common Ratings (PNCR) and Absolute Percentage of Non-Common Ratings (PNCR) in a similarity measure as follows:…”

Section: Single-user Recommender Systems Using Memory-based Collaborative Filteringmentioning

confidence: 99%

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Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

Nam

2022

IEEE Access

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To meet the increasing demand for group activities, single-user recommender systems need to be scaled up so as to provide recommendations to groups of users. This issue is solved by aggregating item preference profiles of individual group members into a single item preference profile, thereby allowing recommendations to be created for this item preference profile. In this paper, we introduce the concept of deep profiles of users, and we propose group recommendation methods based on the aggregation of group members' deep profiles, instead of item preference profiles as in previous studies. The term deep profile refers to the users' profiles that lie deep within the recommendation algorithms. Experiments have shown that group recommendations based on deep profiles give higher efficiency in terms of F1-score and nDCG than those based on item preference profiles. INDEX TERMSCollaborative filtering; group recommender systems; recommender systems I. INTRODUCTION

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Section: Single-user Recommender Systems Using Memory-based Collaborative Filteringmentioning

confidence: 99%

“…where and are the item that user and user have rated, respectively. LM [34] defines a landmark set to represent users instead of the item set. Landmarks are the users with the most observed ratings.…”

Section: Single-user Recommender Systems Using Memory-based Collaborative Filteringmentioning

confidence: 99%

Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

Nam

2022

IEEE Access

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“…They differ in how to process the user‐item matrix. The model‐based algorithms (Khadem & Forghani, 2020; Lima et al, 2020) follow two steps: The algorithm handles several matrices to generate an efficient model for representing the original rating matrix. The generated model is then used to estimate the predictions of active user ratings. The most popular models for making recommendations include Bayesian classifiers (Gao et al, 2020), neural networks (Bandyopadhyay & Thakur, 2020), fuzzy systems (Barzanti et al, 2020), genetic algorithms (GAs) (Moses & Babu, 2020), latent features (Da'u et al, 2020), and matrix factorization (Liu & Ye, 2020). On the other hand, based on the steps outlined below, memory‐based algorithms (Mallik & Sahoo, 2020; Narayanan et al, 2020) use the entire rating matrix to achieve predictions: The prediction ratings of the active user are estimated from the ratings of their neighbours. The similarity metrics are used to measure the distance between two users or two items by their ratings. Memory‐based methods are divided into two main algorithms: User‐based algorithms, where the method for obtaining neighbours works based on the users. Item‐based algorithms, where neighbours are obtained based on the items. There are two main issues in RSs: cold start and sparsity.…”

Section: Introductionmentioning

confidence: 99%

“…They differ in how to process the user-item matrix. The model-based algorithms (Khadem & Forghani, 2020;Lima et al, 2020) follow two steps:…”

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confidence: 99%

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

2022

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The most important subjects in the memory‐based collaborative filtering recommender system (RS) are to accurately calculate the similarities between users and finally finding interesting recommendations for active users. The main purpose of this research is to provide a list of the best items for recommending in less time. The fuzzy‐genetic collaborative filtering (FGCF) approach recommends items by optimizing fuzzy similarities in the continuous genetic algorithm (CGA). In this method, first, the crisp values of user ratings are converted to fuzzy ratings, and then the fuzzy similarities are calculated. Similarity values are placed into the genes of the genetic algorithm, optimized, and finally, they are used in fuzzy prediction. Therefore, the fuzzy system is used twice in this process. Experimental results on RecSys, Movielens 100 K, and Movielens 1 M datasets show that FGCF improves the collaborative filtering RS performance in terms of quality and accuracy of recommendations, time and space complexities. The FGCF method is robust against the sparsity of data due to the correct choice of neighbours and avoids the users' different rating scales problem but it not able to solve the cold‐start challenge.

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“…Moreover, a number of techniques have been developed for memory-based systems, aiming to tackle the deficiencies presented above; these include distributed techniques [14] and optimization methods [15][16][17][18] to improve scalability, density enrichment [19] and coverage increase methods [20] to tackle sparsity, and a multitude of methods to improve rating prediction and recommendation quality [21][22][23][24][25]. Considering all the above, memory-based techniques are a viable approach for building contemporary and efficient recommender systems.…”

Section: Introductionmentioning

confidence: 99%

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

2021

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In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users’ ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of ‘black sheep near neighbours’, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.

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Applying landmarks to enhance memory-based collaborative filtering

Cited by 36 publications

References 31 publications

Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

Profile Aggregation-Based Group Recommender Systems: Moving From Item Preference Profiles to Deep Profiles

Optimization of fuzzy similarity by genetic algorithm in user‐based collaborative filtering recommender systems

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

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