Simultaneous co-clustering and learning to address the cold start problem in recommender systems

Pereira, André Luiz Vizine; Hruschka, Eduardo R.

doi:10.1016/j.knosys.2015.02.016

Cited by 113 publications

(23 citation statements)

References 44 publications

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“…2) Recommender systems based on deep neural networks In many studies, supervised learning-based machine learning approaches have been used to address the cold-start problem in recommender systems [28]- [30]. However, this is difficult because recommending an item under cold-start conditions presents similar problems to processing unlabeled data.…”

Section: A Related Work 1) Item-side Cold-start Problems In Recommenmentioning

confidence: 99%

Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak Supervision

et al. 2020

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Recently, recommender systems have been used in various fields. However, they are still plagued by many issues, including cold-start and sparsity problems. The cold-start problem occurs when users are unable to make recommendations to other users owing to a complete lack of information about certain items. This problem can exist both at the user side and the item side. User-side cold-start problems occur when new users access the systems; item-side cold-start problems occur when new items are added to databases. In this study, we addressed the item-side cold-start problem using the concept of weak supervision. First, a new process for identifying feature based representative reviewers in a rater group was designed. Then, we developed a method to predict the expected preferences for new items by combining content-based filtering and the preferences of representative users. Through extensive experiments, we first confirmed that in comparison to existing methods, the proposed approach provided enhanced accuracy, which was evaluated by determining a mean absolute error for the average ratings. Then, we compared the proposed scheme with the collaborative filtering (CF) and neural CF approaches (NCF). The estimation by the proposed approach was 21% and 38% more accurate than CF and NCF in terms of mean absolute error (MAE), respectively. In future, the proposed framework can be applied in various recommender systems as a core function.

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Section: A Related Work 1) Item-side Cold-start Problems In Recommenmentioning

confidence: 99%

Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak Supervision

et al. 2020

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show abstract

“…Luize et al (2015), for increasing the performance of the system and solving the cold start problem, posed the combination method of both participatory filtering and demographic information. In the study, they used the combination co-clustering algorithm and knowing the machine for solving the cold start problem and have evaluated Movie lens, Jester, http://www.ispacs.com/journals/cacsa/2017/cacsa-00073/ International Scientific Publications and Consulting Services and Netflix dataset [9]. Due to unimportant challenges like scalability, dispersion and users confidence compare with the cold start and films which has been researched till now, the challenges have also been resolved with preprocessing, clustering and classification.…”

Section: Related Workmentioning

confidence: 99%

A Model for Movie Recommender Systems Based on Collaborative Filtering

Heydari¹,

Parvinnia²

2017

Communications on Advanced Computational Science with Applicati

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The recommender systems are used in different fields such as movie, music, social networks and etc. their aim is giving attractive suggestions to users concerning their performance in the system. Content-based methods and participatory filtering are the most popular recommender systems. The participatory method has two main problems. The first one is a cold start of users and the next problem is loyal users believe. In this article, one recommender system has been designed with a combination of the content-based and participatory filtering methods as a boosting system. Besides, that includes the problem solving of a cold start, trusting and attention to the loyal users. The recommender method consists of three steps: 1) the primary classification of all users and assigning a suitable classification to a new user. 2) The determination of an appropriate weight for each characteristic of slightly users and close neighbors of a new user. 3) Making a scores adjacency matrix of the neighbors close to available movie and the scores calculation of new users for each movie. The presented method, concerning demographic information of users, utilizes content-based system capability for primary classification. The gained consequences show that, mean absolute error and the root mean square error for the recommender method compare with Naïve Bayes, C 2 4.5, C M 4.5 and RCA has improved about 8.4%.

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“…The system used the local similarity of biclusters and combined it with the global similarity. Vizine et al (2015) approach combined CF recommendations with demographic information and adapted SCOAL (Simultaneous Co-Clustering and Learning) algorithm. Symeonidis et al (2008) have used biclustering to reveal the duality between users and items.…”

Section: Background Workmentioning

confidence: 99%

Merging user and item based collaborative filtering to alleviate data sparsity

Kant

Mahara

2016

Int J Syst Assur Eng Manag

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Memory based algorithms, generally referred as similarity based Collaborative Filtering (CF) algorithm, is one of the most widely accepted approaches to provide service recommendations. It provides personalized and automated suggestions to customers to select variety of products. Memory based algorithms mainly have two kinds of algorithms: User-based and Item-based algorithms. The User-based CF algorithm recommends items by finding similar users. Contrary to User-based CF, an Item-based CF algorithm recommends items by finding similar items. The core of memory based CF technologies is to calculate similarity among users or items. However, due to inherent sparsity, a large number of entries (ratings) in user-item rating matrix are missing. This results in only few available ratings to make prediction for the unknown ratings. This results in poor prediction quality of the CF algorithm. In this paper a hybrid approach is presented that combines user-based CF and item-based CF. It also leverage the biclustering technique to reduce the dimensionality. The biclustering helps to cluster all users/items into several groups. These clusters are then used to measure users/items similarities based on their respective parent groups. To obtain individual prediction, it adopts the user-based and item-based CF schemes based on the computed similarity respectively. Finally it combines the resultant predictions of each model to make final predictions. Interestingly, experiments demonstrated that the proposed approach outperforms the traditional user-based, item-based and some state of the art recommendation approaches in terms of accuracy of prediction and quality of recommendations.

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Simultaneous co-clustering and learning to address the cold start problem in recommender systems

Cited by 113 publications

References 44 publications

Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak Supervision

Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak Supervision

A Model for Movie Recommender Systems Based on Collaborative Filtering

Merging user and item based collaborative filtering to alleviate data sparsity

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