Improved recommendation based on collaborative tagging behaviors

Zhao, Shiwan; Du, Nan; Nauerz, Andreas; Zhang, Xiatian; Yuan, Quan; Fu, Rongyao

doi:10.1145/1378773.1378843

Cited by 63 publications

(41 citation statements)

References 4 publications

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“…3, Users, items and tags have ternary relationship and this relationship splits into three binary relations. Zhao et al [27] present a novel tag based collaborative filtering approach which uses semantic distance between user generated tags as a measure for finding similarity between users. This approach selects neighbors of user effectively.…”

Section: Web 20 Recommender Systemsmentioning

confidence: 99%

A Survey on the Generation of Recommender Systems

Singh¹,

chuchra²,

Rani³

2017

IJIEEB

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Abstract-In the era of Internet, web is a giant source of information. The constantly growing rate of information in the web makes people confused to decide which product is relevant to them. To find relevant product in today's era is very time consuming and tedious task. Everyday a lot of information is uploaded and retrieved from the web. The web is overloaded with information and it is very essential to cop up with this overloaded and overlooked information. Recommender systems are the solution which can help a user to get relevant information from the bulk of information. Recommender systems provide customized or personalized and non personalized recommendations to interested users. Recommender systems are in its evolution stage. Recommender systems have been evolved from first generation to third generation through second generation. First generation or Web 1.0 recommender systems deal with E-commerce, Second generation or web 2.0 recommender systems use social network and social contextual information for accurate and diverse recommendations, and Third generation recommender systems use location based information or internet of things for generating recommendations. In this paper, three generation of recommender systems and are discussed. Similarity measures and evaluation metrics are used in these generations are also discussed.

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Section: Web 20 Recommender Systemsmentioning

confidence: 99%

A Survey on the Generation of Recommender Systems

Singh¹,

chuchra²,

Rani³

2017

IJIEEB

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“…Other studies detect neighbours through observations like Kim et al, [17], which enrich the user profile with tags of user's friends not included in the user profile based on the observation that two people who share common tags are considered close and may well have interests in common. Also Zhao et al, [33] assume that two users are similar if they share a large number of tags that are strongly related. Other researchers have tried to combine different parameters in order to detect the similarity between users.…”

Section: ) Exploiting the User (Neighbours)mentioning

confidence: 99%

Dynamic enrichment of social users' interests

Mezghani¹,

Zayani²,

Amous³

et al. 2014

2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 13109 Abstract-In a social context, the user is more and more an active contributor for producing social information. Then, he needs a tailored information reflecting his current needs and interests in every period of time. This aims to provide a better adaptation while accessing the information space by integrating users' interests dynamic. Indeed, users' interests may change and become "outdated" through time. So, an interest judged as relevant in a period of time may fluctuate in the next period of time. Moreover, analysing the classic user behaviour to deduce his current interests is a difficult task. In fact, his behaviour isn't always reflecting his real interests. In this paper, we propose a new approach for enriching the user profile in an evolutionary environment such as a social network. The enrichment takes into account: i) the social behaviour and more precisely the tagging behaviour (that reflects user's interests) and ii) the temporal information (that reflects the dynamic evolution of users' interests). Our approach focus on the concept of temperature that reflects the importance of a resource in each period of time. This concept is used to infer common interests of users tagging the same "important" resource. The originality of our approach relies on combining information tags, users and resources in a way that guarantees a better enrichment for the social user profile. Our approach has been tested and evaluated with the Delicious social database and shows interesting precision values.

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“…The cosine similarity metric [4] has been extensively used in the data-mining community and in many settings to compute to distances between either users [5,6] or tags [7,8]. The cosine between two vectors of items is defined as…”

Section: (I) Overlapmentioning

confidence: 99%

Towards a personalized Internet: a case for a full decentralization

Kermarrec

2013

Phil. Trans. R. Soc. A.

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The Web has become a user-centric platform where users post, share, annotate, comment and forward content be it text, videos, pictures, URLs, etc. This social dimension creates tremendous new opportunities for information exchange over the Internet, as exemplified by the surprising and exponential growth of social networks and collaborative platforms. Yet, niche content is sometimes difficult to retrieve using traditional search engines because they target the mass rather than the individual. Likewise, relieving users from useless notification is tricky in a world where there is so much information and so little of interest for each and every one of us. We argue that ultra-specific content could be retrieved and disseminated should search and notification be personalized to fit this new setting. We also argue that users’ interests should be implicitly captured by the system rather than relying on explicit classifications simply because the world is by nature unstructured, dynamic and users do not want to be hampered in their actions by a tight and static framework. In this paper, we review some existing personalization approaches, most of which are centralized. We then advocate the need for fully decentralized systems because personalization raises two main issues. Firstly, personalization requires information to be stored and maintained at a user granularity which can significantly hurt the scalability of a centralized solution. Secondly, at a time when the ‘big brother is watching you’ attitude is prominent, users may be more and more reluctant to give away their personal data to the few large companies that can afford such personalization. We start by showing how to achieve personalization in decentralized systems and conclude with the research agenda ahead.

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Improved recommendation based on collaborative tagging behaviors

Cited by 63 publications

References 4 publications

A Survey on the Generation of Recommender Systems

A Survey on the Generation of Recommender Systems

Dynamic enrichment of social users' interests

Towards a personalized Internet: a case for a full decentralization

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