Cold-Start Recommendation Using Bi-Clustering and Fusion for Large-Scale Social Recommender Systems

Zhang, Daqiang; Hsu, Ching‐Hsien; Chen, Min; Chen, Quan; Xiong, Naixue; Lloret, Jaime

doi:10.1109/tetc.2013.2283233

Cited by 121 publications

(41 citation statements)

References 36 publications

(28 reference statements)

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“…George and Merugu (2005) employed a weighted co-clustering algorithm that simultaneously obtains item and user neighborhoods. Zhang et al (2014b) proposed cloud-based CF approach using biclustering and Fusion (BiFu). Alqadah et al (2014) proposed a collaborative filtering method using biclustering neighborhood approach.…”

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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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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“…For example, call quality is only for mobile phones. It needs in-depth research for such products; (2) the cold-start problem is most prevalent in recommender systems (Zhang et al, 2014). One reason of such issue is the user preferences or behavior history is not clear.…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

Wang

2015

New Review of Hypermedia and Multimedia

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The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinionrich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.

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“…In [10], Zhang et.al, explained the social recommender system based on collaborative filtering, which was limited to give better solution for the cold-start recommendations. Koren et.al, built a multifacetated collaborative filtering model [11] that locates similar items and like-minded users, however, it was ineffective with cold start setting.…”

Section: Iirelated Workmentioning

confidence: 99%

Personalized collaborative filtering recommender system using domain knowledge

Gopalachari

Sammulal

2014

International Conference on Computing and Communication Technologies

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In the current era of web applications such as e-retail business, the web services focused to provide personalized search systems to the targeted user intents based on the navigation patterns. Intelligent collaborative filtering recommender system tries to recommend the web pages considering the similar patterns of the other users along with the usage knowledge of the current user session. This recommender systems strategy lacks of the domain knowledge in comparing the usage patterns of the other users in serving with recommendations. This paper mainly focused on incorporating the domain knowledge and usage knowledge in personalization as well as in comparing the similar user patterns for recommender systems. This novel strategy builds a model to recommend the web pages that can help the new search scenarios and can improve the likelihood of a user towards the host website. Experimental results shown that the proposed novel strategy yields to gain in performance of the recommender system in terms of the quality of the web page recommendations.

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Cold-Start Recommendation Using Bi-Clustering and Fusion for Large-Scale Social Recommender Systems

Cited by 121 publications

References 36 publications

Merging user and item based collaborative filtering to alleviate data sparsity

Merging user and item based collaborative filtering to alleviate data sparsity

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

Personalized collaborative filtering recommender system using domain knowledge

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