Top-N music recommendation framework for precision and novelty under diversity group size and similarity

Chen, Shih-Han; Sou, Sok‐Ian; Hsieh, Hsun-Ping

doi:10.1007/s10844-023-00784-2

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…(1) We introduce a novel rating-based recommendation formulation algorithm that exploits the certainty aspect of rating predictions to generate more accurate and useful recommendations. Since the presented algorithm targets the recommendation formulation stage, for the assessment of the algorithm performance, we utilize pertinent metrics, and more specifically (i), the average rating value of the top-N recommended items, (ii) precision, and (iii) the normalized discounted cumulative gain (NDCG) [7][8][9][10][11]. (3) We analyze the additional computational and storage costs incurred in order to compute, store, and utilize the additional data needed by the proposed algorithm, demonstrating its feasibility and applicability.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering

Margaris,

Sgardelis,

Spiliotopoulos

et al. 2024

BDCC

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Collaborative filtering is a popular recommender system (RecSys) method that produces rating prediction values for products by combining the ratings that close users have already given to the same products. Afterwards, the products that achieve the highest prediction values are recommended to the user. However, as expected, prediction estimation may contain errors, which, in the case of RecSys, will lead to either not recommending a product that the user would actually like (i.e., purchase, watch, or listen) or to recommending a product that the user would not like, with both cases leading to degraded recommendation quality. Especially in the latter case, the RecSys would be deemed unreliable. In this work, we design and develop a recommendation algorithm that considers both the rating prediction values and the prediction confidence, derived from features associated with rating prediction accuracy in collaborative filtering. The presented algorithm is based on the rationale that it is preferable to recommend an item with a slightly lower prediction value, if that prediction seems to be certain and safe, over another that has a higher value but of lower certainty. The proposed algorithm prevents low-confidence rating predictions from being included in recommendations, ensuring the recommendation quality and reliability of the RecSys.

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Section: Introductionmentioning

confidence: 99%

Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering

Margaris,

Sgardelis,

Spiliotopoulos

et al. 2024

BDCC

View full text Add to dashboard Cite

show abstract

Explainable recommender system directed by reconstructed explanatory factors and multi‐modal matrix factorization

Chang,

Zhang,

Cai

2024

Concurrency and Computation

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SummaryMatrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods.

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EqBal-RS: Mitigating popularity bias in recommender systems

Gupta,

Kaur,

Jain

2023

J Intell Inf Syst

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Top-N music recommendation framework for precision and novelty under diversity group size and similarity

Cited by 4 publications

References 38 publications

Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering

Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering

Explainable recommender system directed by reconstructed explanatory factors and multi‐modal matrix factorization

EqBal-RS: Mitigating popularity bias in recommender systems

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