Cold Start Problem in Social Recommender Systems: State-of-the-Art Review

Revathy, V. R.; Pillai, Anitha S.

doi:10.1007/978-981-13-0341-8_10

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…CF recommender systems in general requires users' or items' past ratings to be able to perform its recommendation function excellently, however there are situations when a user past ratings are not available because the user may not have rated any item as in the case of a new user that requires recommendation. These situations are generally referred to as cold start problems (Revathy & Anitha, 2019). Cold start problems can be classified into two basic categories depending on the situation: the user cold start problem and the item cold start problem (Zhu et al, 2020).…”

Section: *Corresponding Authormentioning

confidence: 99%

Addressing Cold Start Problem in Collaborative Filtering using Demographic Data with Entropy-based Methodology

Odumuyiwa¹,

Oloba²

2021

FUOYEJET

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Collaborative filtering based recommender systems (RS) are faced with cold start problem. This problem arises when the RS does not have enough information or opinion about a person or about a product and therefore cannot make recommendation for such person. In this paper, the demographic data of the user such as age, gender, and occupation are utilized as additional sources together with existing users’ rating to tackle the cold start problem by employing the entropy-based methodology to determine the degree of predictability. Experimental results on MovieLens dataset showed that the proposed method gives higher accuracy than other existing demographic based methods. Keywords— Cold Start, Collaborative Filtering, Entropy, Demographic Approach, Recommender Systems

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Section: *Corresponding Authormentioning

confidence: 99%

Addressing Cold Start Problem in Collaborative Filtering using Demographic Data with Entropy-based Methodology

Odumuyiwa¹,

Oloba²

2021

FUOYEJET

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“…They used the hidden interest of the group members to recommend to the new or the target user the one does not have any past history regarding the preferences. The prime focus of this paper [11] is to review all the existing techniques that have been proposed using collaborative filtering method to overcome the cold start drawback. This paper [12] gives an insight on the challenges that are being faced in the group recommender system while recommending to the group members.…”

Section: Related Workmentioning

confidence: 99%

Group Recommendation for Cold Start Users Using Hybrid Recommendation Technique

Kaur¹,

Bathla²

2019

IJEAT

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Recommender system is an data retrieval system that gives customers the recommendations for the items that a customer may be willing to have. It helps in making the search easy by sorting the huge amount of data. We have progressed from the era of paucity to the new era of plethora due to which there is lot of development in the recommender system. In today’s scenario the interaction between the groups of friends, family or colleagues has increased due to the advancement in mobile devices and the social media. So, group recommendation has become a necessity in all kinds of domains. In this paper a system has been proposed using the group recommendation system based on hybrid based filtering method to overcome the cold start user issue which arises when a new user signs in and he/she doesn’t have any past records. So, the recommender system does not have enough information related to the user to recommend an item which will be of his/her interest. The dataset has been taken from the MovieLens is used in the experiment.

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“…However, there are different approximations to solve it. [153,[452][453][454] . Solving this would reduce the time needed to provide relevant recommendations and increase the appreciation levels.…”

Section: Appreciationmentioning

confidence: 99%

Health recommender systems for behavior change

Hors-Fraile

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Cold Start Problem in Social Recommender Systems: State-of-the-Art Review

Cited by 9 publications

References 24 publications

Addressing Cold Start Problem in Collaborative Filtering using Demographic Data with Entropy-based Methodology

Addressing Cold Start Problem in Collaborative Filtering using Demographic Data with Entropy-based Methodology

Group Recommendation for Cold Start Users Using Hybrid Recommendation Technique

Health recommender systems for behavior change

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