Hierarchical Agglomerative Clustering for Agent-Based Dynamic Collaborative Filtering

Uchyigit, Gulden; Clark, Keith

doi:10.1007/978-3-540-28651-6_123

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…Several hybrid recommender systems which employ clustering algorithms have been proposed in the literature to overcome recommender systems' shortcomings [34,36,38]. In Ref.…”

Section: Clustering In Recommender Systemsmentioning

confidence: 99%

“…To overcome the cold start problem, the clustering concept is used in collaborative filtering to provide more accurate predictions [8,11,13,23,[34][35][36][37][38][39][40][41]. Although it has been shown that clustering-based CF approaches are useful methods to deal with the cold start problem, these methods also suffer from several shortcomings.…”

Section: Introductionmentioning

confidence: 99%

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An effective trust-based recommendation method using a novel graph clustering algorithm

Moradi

Ahmadian

Akhlaghian

2015

Physica A: Statistical Mechanics and its Applications

121

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“…Several hybrid recommender systems which employ clustering algorithms have been proposed in the literature to overcome recommender systems' shortcomings [34,36,38]. In Ref.…”

Section: Clustering In Recommender Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An effective trust-based recommendation method using a novel graph clustering algorithm

Moradi

Ahmadian

Akhlaghian

2015

Physica A: Statistical Mechanics and its Applications

121

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“…Different hierarchical clustering algorithms have been used to resolve the recommendation problem . In the work of Chee et al, the authors proposed a clustering approach that recursively splits datasets into clusters by constructing a binary tree of clusters using K‐means clustering algorithm, where the root represents the full dataset.…”

Section: State Of the Artmentioning

confidence: 99%

Social recommendation: A user profile clustering‐based approach

Ouaftouh

Zellou

Idri

2019

Concurrency and Computation

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The recommendation in information systems is a specific form of information filtering that aims to present the relevant information interesting the user. This technique is used in different contexts such as social networking, e-commerce and information retrieval. Generally, existing recommender system techniques implement collaborative filtering by deducing a part of user interests from the preferences of other users with similar profiles. Many techniques can be used to implement Collaborative Filtering such as Bayesian Networks, latent semantic, and clustering.We present in this work a novel clustering approach using a modified partitional algorithm. We propose a user model that integrates the relevant user information and a clustering algorithm that generates groups of similar user profiles by implementing a profile similarity function. The proposed approach is then evaluated based on a set of user profiles data corresponding to the context of an e-commerce website. KEYWORDSclustering algorithm, recommender systems, similarity measures, user profile INTRODUCTIONRecent technologies offer access to large masses of information. In order to help the user get relevant information that specifically concerns him/her, personalization is the appropriate solution. The concept of personalization, also called adaptation, is an extension of a research trend that has been actively used since the beginning of the 1980s in human-machine interaction. 1 Personalization can be as much about the content (data, information, document, etc) as about the container (IHM, interaction platform, communication mode, communication channel, etc). This concept is often used in a variety of domains, ie, Information Retrieval, e-commerce, tutoring systems, etc.In order to offer the user the personalized content adapted to his/her expectations, it is necessary to identify his/her needs and preferences. 2-4A personalized system has to satisfy preferences of the user based on his profile. 5 The user profile is built using information gathered about the user and his/her use of the system. Offering personalized content adapted to user's expectations implies identifying his/her needs and preferences. [2][3][4] The implementation of personalization can be performed through different techniques, ie, personalizing the appearance of the interface, filtering the incoming information or recommending content to the user. In this paper, we focus on the last option that is recommending the content to the user.It has been shown recently 6 that a normal user can lose interest after 60 to 90 seconds of choosing through reviewing 10 to 20 items on one or two screens. 6 That points to the risks of the user abandoning the system if he/she doesn't find what interests him/her. Recommendation aims to ensure that each user will find something corresponding to his/her preferences.In this work, a novel approach is proposed for a user profile clustering based recommendation. This approach considers a global user model represented with a keyword vector model structure usin...

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“…This paper employs a hierarchical agglomerative clustering [73] in order to accommodate aforementioned needs and…”

Section: Clusteringmentioning

confidence: 99%

Multiagent Approach to Fuzzy-Linguistic Knowledge Integration

Modliński¹,

Popek²

2018

CMST

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The paper aims to give at least a partial answer to an urgent need for knowledge processing systems equipped with semantic capabilities. One of the crucial goals is to reflect inner computational models and numerical data outside of the system by presenting linguistic statements easily understood by a non-expert user. The paper follows a motivational scenario and presents a layered approach to knowledge integration. The fundamental rationale behind the proposed approach is that a degree of inconsistency of the whole body of knowledge should be incorporated into the formed summary and conveyed to the external user of the system. The paper deals with a practically important problem of processing modal epistemic statements about an object exhibiting some set of fuzzy properties. The statements represent distributed knowledge of some agent population and are represented on the level of a semi-natural language. In particular, the paper describes an approach to two-level fuzzy-linguistic knowledge integration based on the consensus-theory and clustering methods. In particular, it discusses the difference between the in-cluster level and the cross-cluster level. While this paper considers an environment limited to a single object with multiple properties, it is directly extendable to environments with multiple objects. The reduction is purely technical as it allows for a simplification of a notation and presented descriptions.

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Hierarchical Agglomerative Clustering for Agent-Based Dynamic Collaborative Filtering

Cited by 9 publications

References 4 publications

An effective trust-based recommendation method using a novel graph clustering algorithm

An effective trust-based recommendation method using a novel graph clustering algorithm

Social recommendation: A user profile clustering‐based approach

Multiagent Approach to Fuzzy-Linguistic Knowledge Integration

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