User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation

Uyangoda, Lasitha; Ahangama, Supunmali; Ranasinghe, Tharindu

doi:10.1109/icdim.2018.8847002

Cited by 15 publications

(8 citation statements)

References 11 publications

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“…Furthermore, we illustrate the characteristics of our model, comparing with the state-of-the-art models. In the future work, we present a exploring research with incorporating additional information [57], [58] into BLFM to solve cold start problem [59], [60]. By combining prior constraint and posterior expectation estimation, BLFM is more robust against overfitting.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Fang,

Zhang,

et al. 2020

Preprint

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Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item in that space and can be efficiently solved by least square methods with optimal estimation. However, such optimal estimation methods are prone to overfitting due to the extreme sparsity of useritem interactions. In this paper, we propose a Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on observed user-item interactions, we build a probabilistic factor model in which the regularization is introduced via placing prior constraint on latent factors, and the likelihood function is established over observations and parameters. Then we draw samples of latent factors from the posterior distribution with Variational Inference (VI) to predict expected value. We further make an extension to BLFM, called BLFMBias, incorporating user-dependent and item-dependent biases into the model for enhancing performance. Extensive experiments on the movie rating dataset show the effectiveness of our proposed models by compared with several strong baselines.

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Section: Discussion and Related Workmentioning

confidence: 99%

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Fang,

Zhang,

et al. 2020

Preprint

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“…Sohail et al [15] present an opinion miningbased recommendation technique to provide the university students with promising books for their syllabus; however, this technique could be subjectively biased. Uyangoda et al [16] apply a user-profile-feature-based approach to improve the recommender system with few user records. Dai et al [17] propose a feature-based bayesian task recommendation scheme to overcome the challenge of emerging recommendations, but the scheme cannot address the changes in users' interests.…”

Section: Knowledge Graph-based Recommendationmentioning

confidence: 99%

Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Liu

et al. 2022

Int J Comput Intell Syst

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Resource recommendation is extremely challenging under low-resource conditions because representation learning models require sufficient triplets for their training, and the presence of massive long-tail resources leads to data sparsity and cold-start problems. In this paper, an industrial knowledge graph is developed to integrate resources for manufacturing enterprises, and we further formulate long-tail recommendations as a few-shot relational learning problem of learning-to-recommend resources with few interactions under low-resource conditions. First, an industrial knowledge graph is constructed based on the predesigned resource schema. Second, we conduct schema-based reasoning on the schema to heuristically complete the knowledge graph. At last, we propose a multi-head attention-based meta relational learning model with schema-based reasoning to recommend long-tail resources under low-resource conditions. With the IN-Train setting, 5-shot experimental results on the NELL-One and Wiki-One datasets achieve average improvements of 28.8 and 13.3% respectively, compared with MetaR. Empirically, the attention mechanism with relation space translation learns the most important relations for fast convergence. The proposed graph-based platform specifies how to recommend resources using the industrial knowledge graph under low-resource conditions.

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“…It is only after a while when the system 'warms up' that it can perform well. This is addressed in [20], an approach where the relationship of user feature-scores obtained from userproduct interaction from the ratings to optimise the prediction algorithm's input parameters used in the recommender system which further improves correctness of the predictions when the user records were considerably less. These methods were experimented on the MovieLens dataset and it showed to greatly reduce the drawback in collaborative filtering with an accuracy increase of 8.4% when compared with the standard collaborative approach.…”

Section: Literature Surveymentioning

confidence: 99%

Ensemble Based Hybrid Recommender Systems

Prathima¹,

Anjana²,

Apoorva³

et al. 2020

IJITEE

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In the past few years, the advent of computational and prediction technologies has spurred a lot of interest in recommendation research. Content-based recommendation and collaborative filtering are two elementary ways to build recommendation systems. In a content based recommender system, products are described using keywords and a user profile is developed to enlist the type of products the user may like. Widely used Collaborative filtering recommender systems provide recommendations based on similar user preferences. Hybrid recommender systems are a blend of content-based and collaborative techniques to harness their advantages to maximum. Although both these methods have their own advantages, they fail in ‘cold start’ situations where new users or products are introduced to the system, and the system fails to recommend new products as there is no usage history available for these products. In this work we work on MovieLens 100k dataset to recommend movies based on the user preferences. This paper proposes a weighted average method for combining predictions to improve the accuracy of hybrid models. We used standard error as a measure to assign the weights to the classifiers to approximate their participation in predicting the recommendations. The cold start problem is addressed by including demographic data of the user by using three approaches namely Latent Vector Method, Bayesian Weighted Average, and Nearest Neighbor Algorithm.

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User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation

Cited by 15 publications

References 11 publications

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Ensemble Based Hybrid Recommender Systems

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