Euclidean embedding with preference relation for recommender systems

• Published in: Multimedia tools and applications 2024, Vol.83 (42), p.89795-89815
• Main Authors: Yannam, V Ramanjaneyulu, Kumar, Jitendra, Babu, Korra Sathya, Patra, Bidyut Kumar
• Format: Article
• Language: English
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Embedding; Factorization; Multimedia Information Systems; Ratings; Ratings & rankings; Recommender systems; Special Purpose and Application-Based Systems; System effectiveness
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18885-7

Recommender systems (RS) help users pick the relevant items among numerous items that are available on the internet. The items may be movies, food, books, etc. The Recommender systems utilize the data that is fetched from the users to generate recommendations. Usually, these ratings may be explicit or implicit. Explicit ratings are absolute ratings that are generally in the range of 1 to 5. While implicit ratings are derived from information like purchase history, click-through rate, viewing history, etc. Preference relations are an alternative way to represent the users’ interest in the items. Few recent research works show that preference relations yield better results compared to absolute ratings. Besides, in RS, the latent factor models like Matrix Factorization (MF) give accurate results especially when the data is sparse. Euclidean Embedding (EE) is an alternative latent factor model that yields similar results as MF. In this work, we propose a Euclidean embedding with preference relation for the recommender system. Instead of using the inner product of items and users’ latent factors, Euclidean distances between them are used to predict the rating. Preference Relations with Matrix Factorization (MFPR) produced better recommendations compared to that of traditional matrix factorization. We present a collaborative model termed EEPR in this work. The proposed framework is implemented and tested on two real-world datasets, MovieLens-100K and Netflix-1M to demonstrate the effectiveness of the proposed method. We utilize popular evaluation metric for recommender systems as precision@K. The experimental outcomes show that the proposed model outperforms certain state-of-the-art existing models such as MF, EE, and MFPR.