Feature weighting in content based recommendation system using social network analysis

Debnath, Sudip; Ganguly, Niloy; Mitra, Pabitra

doi:10.1145/1367497.1367646

Cited by 203 publications

(94 citation statements)

References 1 publication

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“…A combination of collaborative filtering content based recommendation system is introduced by Souvik et al [145] to recommend users based on their interest. Sparse regression and isometric projection are dominant and based on the users' posts in social media and by applying collaborative filtering, the user's interest is recorded.…”

Section: Fig 4 Components Of Geo-taggingmentioning

confidence: 99%

An Overview on User Profiling in Online Social Networks

Vasanthakumar¹,

Sunithamma²,

Shenoy³

et al. 2017

IJAIS

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Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors.

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Section: Fig 4 Components Of Geo-taggingmentioning

confidence: 99%

An Overview on User Profiling in Online Social Networks

Vasanthakumar¹,

Sunithamma²,

Shenoy³

et al. 2017

IJAIS

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“…It uses consumer's evaluation to calculate similarity measure but it needs a large of information. Proposed system is a hybrid form [5] that takes advantage of the two implementations.…”

Section: Proposed Business Matching Systemmentioning

confidence: 99%

An Intelligent Matching System for the Products of Small Business/Manufactures with the Celebrities

Jeong

Son

et al. 2016

MATEC Web of Conferences

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Abstract. With the development of ICT, e-business have brought consumers opportunities to choose a wider range of products to purchase. However, these opportunities made it difficult for them to make a choice. Under this circumstance, product recommendation systems have arisen, helping consumers with purchase decision. In this paper, an intelligent matching system is proposed to connect small business/manufactures with celebrities. Previous matching/recommendation systems using narrowing approach based on limited information or using widening approach based on users' information. But these approaches have many restricts. The proposed matching system produces suitable celebrity candidate set to advertise target product of small business/manufactures based on relationship graph which can compare the similarity of each celebrity.

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“…The effectiveness of content-based information filtering paradigm has been proven for applications locating textual documents relevant to a topic. Particularly in recommender systems, contentbased methods [1,4,7] enable accurate comparison between different textual or structural items, and hence recommend items similar to a user's consumption history. However, content-based filtering approaches suffer from multiple drawbacks, e.g., strong dependence on the availability of content, ignoring the contextual information of recommendation, and etc.…”

Section: Related Workmentioning

confidence: 99%

Taxonomy-Oriented Recommendation towards Recommendation with Stage

Hong

2012

Web Technologies and Applications

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Abstract.In some E-commerce recommender systems, a special class of recommendation involves recommending items to users in a life cycle. For example, customers who have babies will shop on Amazon within a relatively long period, and purchase different products for babies within different growth stages. Traditional recommendation algorithms cannot effectively resolve the situation with a life cycle. In this paper, we model users' behavior with life cycles by employing hand-crafted item taxonomies, of which the background knowledge can be tailored for the computation of personalized recommendation. In particular, our method first formalizes a user's long-term behavior using the item taxonomy, and then identify the exact stage of this user. By incorporating collaborative filtering into our method, we can easily provide a personalized item list to this user through other similar users within the same stage. An empirical evaluation conducted on a purchasing data collection obtained from Amazon demonstrates the efficacy of our proposed method.

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Feature weighting in content based recommendation system using social network analysis

Cited by 203 publications

References 1 publication

An Overview on User Profiling in Online Social Networks

An Overview on User Profiling in Online Social Networks

An Intelligent Matching System for the Products of Small Business/Manufactures with the Celebrities

Taxonomy-Oriented Recommendation towards Recommendation with Stage

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