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Recommendation based on object typicality

Yi Cai, Ho-fung Leung, Qing Li, Jie Tang, Juanzi Li
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
In this paper, we investigate recommendation systems from a new perspective based on object typicality and propose a novel typicalitybased recommendation approach.  ...  Current recommendation methods are mainly classified into contentbased, collaborative filtering and hybrid methods. These methods are based on similarity measurements among items or users.  ...  CONCLUSION In this paper, we investigate the recommendation system from a new perspective and present a novel recommendation method based on object typicality.  ... 
doi:10.1145/1871437.1871664 dblp:conf/cikm/CaiLLTL10 fatcat:baeatxsvhvah5kwbxl24ll56wa

Review Paper on Next Generation Collaborative Filtering of Data Based on Typicality Method

2015 International Journal of Science and Research (IJSR)  
In item based CF method, main task is to provide a user with the recommendation on an item based on the item with high correlations.  ...  Here, we have proposed novel efficient Expectation Maximization (EM) Clustering and typicality-based collaborative filtering recommendation (EmTyCo).  ...  All these works focus on developing methods to calculate object typicality in concepts. There is no work on integrating typicality in collaborative filtering recommendation.  ... 
doi:10.21275/v4i12.22121501 fatcat:yr4qmddwyzb5rfi32hqzh5zm2e

Typicality-Based Collaborative Filtering Recommendation

Yi Cai, Ho-fung Leung, Qing Li, Huaqing Min, Jie Tang, Juanzi Li
2014 IEEE Transactions on Knowledge and Data Engineering  
To the best of our knowledge, there has been no prior work on investigating CF recommendation by combining object typicality.  ...  In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo.  ...  Thus, in this paper, we borrow the idea of object typicality from cognitive psychology and propose a typicality-based CF recommendation approach named TyCo.  ... 
doi:10.1109/tkde.2013.7 fatcat:2wlnlgksfrdj7lc3f7vdkcdwj4

User based Recommender Systems using Implicative Rating Measure

Lan Phuong, Hung Huu, Hiep Xuan
2017 International Journal of Advanced Computer Science and Applications  
This paper proposes the implicative rating measure developed on the typicality measure. The paper also proposes a new recommendation model presenting the top N items to the active users.  ...  The proposed model is based on the user-based collaborative filtering approach using the implicative intensity measure to find the nearest neighbors of the active users, and the proposed measure to predict  ...  Typicality To measure the typicality of an object i for the formation of the relationship a  b, the typicality is proposed as (5) [9] .  (i,a  b)=1 -d 1 (i,a  b) / max jE d 1 (j,a  b)   ... 
doi:10.14569/ijacsa.2017.081105 fatcat:x2zcs5uwsrfrjdmspravg4en7i

Modern Approaches to Building Recommender Systems for Online Stores

Lyudmila Onokoy, Jurijs Lavendels
2019 Applied Computer Systems  
Of greatest interest are the criteria for selecting effective methods for specific online stores and the authors' concept of a typical recommender system of electronic commerce.  ...  Much attention is paid to modern methods of building recommender systems, analysing their strengths and weaknesses.  ...  The concept of a typical recommender system developed by the authors is based on the use of the proven collaborative and content filtering methods.  ... 
doi:10.2478/acss-2019-0003 fatcat:7erpfcv7qvfynbc7ea56eezjze

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

Outmane Bourkoukou, Essaid El Bachari
2018 JOIV: International Journal on Informatics Visualization  
Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in  ...  Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms.  ...  Several researches on personalized learning are focused also on the learner profile based on learning style for recommendation [8, 23, 24] .  ... 
doi:10.30630/joiv.2.4.158 fatcat:qbclyxa7b5dcjesftp3i7p6a6m

PLORS: a personalized learning object recommender system

Hazra Imran, Mohammad Belghis-Zadeh, Ting-Wen Chang, Kinshuk, Sabine Graf
2015 Vietnam Journal of Computer Science  
Generally, LMSs do not cater individual learners' needs based on their profile.  ...  However, such LMS typically use a "one size fits all" approach without considering individual learner's profile.  ...  Typically, the courses in LMS consists of learning objects (LOs).  ... 
doi:10.1007/s40595-015-0049-6 fatcat:t5g37khvrrdmxogdyuhi5kkgii

Efficient, Interactive Recommendation of Mashup Composition Knowledge [chapter]

Soudip Roy Chowdhury, Florian Daniel, Fabio Casati
2011 Lecture Notes in Computer Science  
We specifically focus on mashups and browser-based modeling tools, a domain that increasingly targets also people without profound programming experience.  ...  We describe an architecture and a pattern knowledge base that are distributed over client and server and a set of client-side search algorithms for the retrieval of step-by-step recommendations.  ...  The former approach is based on exact matches with the object, the latter leverages on similarity search.  ... 
doi:10.1007/978-3-642-25535-9_25 fatcat:m3ykzn3mnzcszcvwa2mwwwipzm

Collaborative filtering with diffusion-based similarity on tripartite graphs

Ming-Sheng Shang, Zi-Ke Zhang, Tao Zhou, Yi-Cheng Zhang
2010 Physica A: Statistical Mechanics and its Applications  
We propose a measure of user similarity based on his preference and tagging information.  ...  In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags.  ...  Since the typical length for recommendation list is tens, our experimental study focuses on the interval L ∈ [10, 100] .  ... 
doi:10.1016/j.physa.2009.11.041 fatcat:cfooqrbr5rfx7jm7iafsglkufe

Recommender Systems as Multistakeholder Environments

Himan Abdollahpouri, Robin Burke, Bamshad Mobasher
2017 Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17  
In research practice, recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user.  ...  However, in many recommendation domains, the user for whom recommendations are generated is not the only stakeholder in the recommendation outcome.  ...  Recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user.  ... 
doi:10.1145/3079628.3079657 dblp:conf/um/AbdollahpouriBM17 fatcat:4fxewwxu6fcbrnauhnxh5pkysq

Implicative Rating-Based Hybrid Recommendation Systems

Lan Phuong Phan, Hung Huu Huynh, Hiep Xuan Huynh
2018 International Journal of Machine Learning and Computing  
Index Terms-Implicative rating measure, hybrid recommendation model, user-based collaborative filtering, association rule.  ...  The paper also proposes the hybrid recommendation model -the combination of the user-based collaborative filtering approach and the association rule based approach using the implicative rating measures  ...  Typicality Measure The typicality measure is proposed as (5) [7] to calculate the typicality of an object for the formation of the relationship  , ) , ( max ) , ( 1 ) , ( 1 1 b a j d b a i d b a i E  ... 
doi:10.18178/ijmlc.2018.8.3.691 fatcat:qofqhdk4zzh4touvbmcp34mb4i

On Efficient Link Recommendation in Social Networks Using Actor-Fact Matrices

Michał Ciesielczyk, Andrzej Szwabe, Mikołaj Morzy
2015 Scientific Programming  
Our recent findings unanimously show that the choice of network representation model has an important and measurable impact on the quality of recommendations.  ...  However, relatively little scientific attention has been paid to the impact that various data representation models have on the performance of recommendation algorithms.  ...  The main objective of link recommendation and prediction is to predict, based on the historical data, unobserved relationships and interactions between actors of a social network [3] .  ... 
doi:10.1155/2015/450215 fatcat:6khpmswxyba7dir54gxk4tbjve

Context-Based User Typicality Collaborative Filtering Recommendation

Jinzhen Zhang, Qinghua Zhang, Zhihua Ai, Xintai Li
2021 Human-Centric Intelligent Systems  
This paper proposes a novel context-based user typicality collaborative filtering recommendation algorithm (named CBUTCF), which combines contextual information with user typicality to alleviate the data  ...  But it does not consider the impact of contextual information on user typicality.  ...  [5] borrowed the idea of object typicality from cognitive psychology and proposed a typicality-based CF recommendation method.  ... 
doi:10.2991/hcis.k.210524.001 fatcat:mkogj65355aodmpl56tanct5pe

A Framework to Provide Personalization in Learning Management Systems through a Recommender System Approach [chapter]

Hazra Imran, Quang Hoang, Ting-Wen Chang, Kinshuk, Sabine Graf
2014 Lecture Notes in Computer Science  
To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners, not only based on their individual profile but also based on what worked well for  ...  The proposed framework is designed with the goal of presenting a flexible integration model which can provide personalization by automatically suggesting learning objects to learners based on their current  ...  The system describes the learning resources based on learning object metadata and the leaners profile based on PAPI [17] .  ... 
doi:10.1007/978-3-319-05476-6_28 fatcat:li2hqt3ryjhaziiue5ip37aszq

Let the Machine Decide: When Consumers Trust or Distrust Algorithms

Noah Castelo, Maarten W. Bos, Donald Lehmann
2019 NIM Marketing Intelligence Review  
In general, consumers tend to rely more on algorithms they are already familiar with. For instance, algorithm-based movie recommendations on Netflix are quite convenient.  ...  Subjective tasks are entrusted to humans more than to machines In one experiment we found that consumers are equally likely to click on ads for algorithm-based and human-based financial advice.  ... 
doi:10.2478/nimmir-2019-0012 fatcat:ct2j2gpmcrejxfcl24ydymsa7e
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