N -dimensional Markov random field prior for cold-start recommendation

Peng, Furong; Lu, Jianfeng; Wang, Yongli; Xu, Richard Yi Da; Ma, Chao; Yang, Jingyu

doi:10.1016/j.neucom.2015.12.099

Cited by 22 publications

(9 citation statements)

References 30 publications

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“…Although artificial intelligence and machine learning algorithms in recommendation systems especially clustering algorithms are emerging techniques, the main problems are the computation times necessary for defining the relations among users or items that can be regarded as performance issue of the recommender systems and potentially useful information might be lost in reducing dimensionality of the user and product space that leads to low quality recommendations [25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

An Effective Collaborative User Model Using Hybrid Clustering Recommendation Methods

Najafabadi¹,

Mohamed²,

Nair³

et al. 2021

ISI

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Collaborative Filtering (CF) has been known as the most successful recommendation technique in which recommendations are made based on the past rating records from like-minded users. Significant growth of users and items have negatively affected the efficiency of CF and pose key issues related to computational aspects and the quality of recommendation such as high dimensionality and data sparsity. In this study, a hybrid method was proposed and was capable to solve the mentioned problems using a neighborhood selection process for each user through two clustering algorithms which were item-based k-means clustering and user-based Fuzzy Clustering. Item-based k-means clustering was applied because of its advantages in computational time and hence it is able to address the high dimensionality issues. To create user groups and find the correlation between users, we employed the user-based Fuzzy Clustering and it has not yet been used in user-based CF clustering. This clustering can calculate the degree of membership among users into set of clustered items. Furthermore, a new similarity metric was designed to compute the similarity value among users with affecting the output of user-based Fuzzy Clustering. This metric is an alternative to the basic similarity metrics in CF and it has been proven to provide high-quality recommendations and a noticeable improvement on the accuracy of recommendations to the users. The proposed method has been evaluated using two benchmark datasets, MovieLens and LastFM in order to make a comparison with the existing recommendation methods.

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Section: Related Workmentioning

confidence: 99%

An Effective Collaborative User Model Using Hybrid Clustering Recommendation Methods

Najafabadi¹,

Mohamed²,

Nair³

et al. 2021

ISI

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“…Markov's chain has been used coupled with matrix factorization to infer and accurately predict new users' preferences. The study of [41] enhanced matrix factorization prediction capability by integrating an n-dimensional Markov random field prior (mrf-MF) to cope with three types of cold-start problem: recommending new users to existing items; recommending new items to existing users; recommend new items to new users. First, a specific neighbors system for user attributes such as age, occupation of users and genre, release year of items is defined followed by conditional distribution of latent profiles.…”

Section: Amentioning

confidence: 99%

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

et al. 2021

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Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations. Existing studies that reviewed and examined cold start in recommender systems have not explained the process of deriving and obtaining the auxiliary information needed for cold start recommendation. This study surveys the existing literature in order to explain the various approaches and techniques employed by researchers and the challenges associated with deriving and obtaining the auxiliary information necessary for cold start recommendation. Results show that auxiliary information for cold start recommendation is obtained by adapting traditional filtering and matrix factorization algorithms typically with machine learning algorithms to build learning prediction models. The understanding of similar or connected user profiles can be used as auxiliary information for building cold start user profile to enable similar recommendations in social networks. Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations. The key challenges of the process for obtaining the auxiliary information involve: (1) two separate recommendation processes of conversion from pure cold start to warm start before eliciting the auxiliary information; (2) the obtained implicit auxiliary information is usually ranked and sieved in order to select the top rated and reliable auxiliary information for the recommendation. This study also found that cold start user recommendation has frequently been researched in the entertainment domain, typically using music and movie data, while little research has been carried out in educational institutions and academia, or with cold start for mobile applications.

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“…7 gives details). Based on Markov random field [184], Mrf-MF [185] hypothesizes that the prior distributions of U, V are related to each user's neighborhood (Tab. 7 gives details).…”

Section: Models Based On Bayesian Analysismentioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

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Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

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N -dimensional Markov random field prior for cold-start recommendation

Cited by 22 publications

References 30 publications

An Effective Collaborative User Model Using Hybrid Clustering Recommendation Methods

An Effective Collaborative User Model Using Hybrid Clustering Recommendation Methods

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Recommender systems based on graph embedding techniques: A comprehensive review

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