Alleviating the cold-start problem of recommender systems using a new hybrid approach

Basiri, Javad; Shakery, Azadeh; Moshiri, Behzad; Hayat, Morteza Zi

doi:10.1109/istel.2010.5734161

Cited by 30 publications

(11 citation statements)

References 15 publications

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“…It is concluded after experimental results that the sparsity in the data is effectively addressed and also significance improvement is noted in the recommendation [14]. It was not the only hybrid model used previously but number of researches suggested hybrid model as one such model was introduced in which collaborative filtering, content based and demographic based models were infused to address the cold start problem [15].…”

Section: A Literature Reviewmentioning

confidence: 99%

A Proposed Method to Solve Cold Start Problem using Fuzzy user-based Clustering

Duja¹,

Niu²,

Ahmed³

et al. 2020

IJACSA

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With the elevation of the online accessibility to almost everything, many logics, systems and algorithms have to be revised to match the pace of the trends among the socialized networks. One such system; recommendation system has become very important as far as the socialized networks are concerned. In such paced and vibrant environment of the online accessibility and availability to heavy and large amount of data uploaded to the internet such as, movies, books, research articles and much more. The method of recommendation where provides the socialized networks between the operators, at the same instance, it provides references for the users to asses other users that effects their socialized relation directly or indirectly. Collaborative filtering is the technique used for recommending the same taste of picks to that of the user, and it is accomplished by the user's mutual collaboration, this technique is mostly used by the social networking sites. Nowadays this technique is not only popular but common for recommending the data to the user; meanwhile it also motivates the researchers to find the more effective system and algorithm so that the user's satisfaction can be achieved by recommending them the data according to their search history. This paper suggests the CF (Collaborative Filtering) model that is based on the user's truthful information applied by the FCM (Fuzzy C-means) clustering. This study proposes that the fuzzy truthful information of the user is to be combined with rating of the content by other users to produce a recommender system formula with a coupled coefficient with new parameters. To achieve the results the Data set of Movie Lens is included in the study which shows significant improvement in the recommendation subjected to the condition of cold start.

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Section: A Literature Reviewmentioning

confidence: 99%

A Proposed Method to Solve Cold Start Problem using Fuzzy user-based Clustering

Duja¹,

Niu²,

Ahmed³

et al. 2020

IJACSA

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show abstract

“…The authors argue that it is more reasonable to set greater similarity between users who have positively evaluated a similar number of items than between users for which the number of items is very different. Basiri et al [3] apply all the available information for each user to create an ordered weighted averaging operator [56] that is used to make recommendations. The operator uses a set of weights associated with each recommendation technique (CF, CBF, and DF) and their possible combinations to make predictions.…”

Section: Related Workmentioning

confidence: 99%

“…It was originally built to support the Netflix Prize, 3 and has more than 100,000,000 ratings made by 480,000 users. Data were collected from October 1998 to December 2005 on a scale from 1 to 5.…”

Section: Netflixmentioning

confidence: 99%

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

Pereira

Hruschka

2015

Knowledge-Based Systems

110

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“…Several ratings are, thus, required from the users before the system gives useful recommendations. This is known as the cold-start problem (Basiri et al, 2010). Content-based approaches (Pazzani and Billsus, 2007) recommend items by taking into account the properties of the activities that users have enjoyed previously and only items closely related to those the user liked in the past are recommended.…”

Section: Ontology In Tourism Systemsmentioning

confidence: 99%