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Adapting vector space model to ranking-based collaborative filtering

Shuaiqiang Wang, Jiankai Sun, Byron J. Gao, Jun Ma
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
In this study, we seek accuracy improvement of ranking-based CF through adaptation of the vector space model, where we consider each user as a document and her pairwise relative preferences as terms.  ...  Collaborative filtering (CF) is an effective technique addressing the information overload problem.  ...  ADAPTING VECTOR SPACE MODEL TO RANKING-BASED CF In this section, we adapt the vector space model to ranking-based CF, where users are considered as documents and relative preferences are considered as  ... 
doi:10.1145/2396761.2398458 dblp:conf/cikm/WangSGM12 fatcat:tcnhgldkdbg5nchzmyksjx2f74


Shuaiqiang Wang, Jiankai Sun, Byron J. Gao, Jun Ma
2014 ACM Transactions on Intelligent Systems and Technology  
In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model.  ...  Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating-based and ranking-based.  ...  CONCLUSION In this paper, we have proposed VSRank, a framework for adapting the vector space model to ranking-based collaborative filtering for improved recommendation accuracy.  ... 
doi:10.1145/2542048 fatcat:b3qynid4ivdcpmf5z5xjlylxqa

Deep Neural Architecture for News Recommendation

Vaibhav Kumar, Dhruv Khattar, Shashank Gupta, Manish Gupta, Vasudeva Varma
2017 Conference and Labs of the Evaluation Forum  
The key factor in user-item based collaborative filtering is to identify the interaction between user and item features.  ...  We then use a ranking based objective function to learn the parameters of the network. We also use the content of the news articles as features for our model.  ...  Finally, in user-item based collaborative filtering, both the users and the items are projected into a common vector space based on the user-item matrix and then the item and user representation are combined  ... 
dblp:conf/clef/KumarKG0V17 fatcat:j5bdo23iojbmjlvmmbqwvqzjkm

A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on RSS Contents

Rebecca A. Okaka, Waweru Mwangi, George Okeyo
2016 International Journal of Computer Applications Technology and Research  
Two traditional methods used to develop recommender systems are content-based and collaborative filtering.  ...  This paper deals with issues related to the design and evaluation of a personalized hybrid recommender system that combines content-based and collaborative filtering methods to improve the precision of  ...  This hybrid approach adapts the Vector Space Model (VSM) in both CBF and CF, uses ranking algorithm Term Frequency Inverse Document Frequency (TFIDF) and cosine similarity measure to find the relationships  ... 
doi:10.7753/ijcatr0512.1006 fatcat:konjzcdnvnekrdasuahvsieuda

Improving Ranking-based Recommendation by Social Information and Negative Similarity

Ying Liu, Jiajun Yang
2015 Procedia Computer Science  
Firstly, a novel similarity measure is proposed to make better use of negative similarity; secondly, social network information is integrated into the model to smooth ranking.  ...  Recently, ranking-based algorithms have been proposed and widely used, which use ranking to present the user preference rather than rating scores.  ...  VSRank adapts vector space model to collaborative filtering algorithm. It regards the users as documents and the pairwise relative preferences between items as words.  ... 
doi:10.1016/j.procs.2015.07.164 fatcat:cdsdsludjrgalm32pfg2j3nqfa

Bridging memory-based collaborative filtering and text retrieval

Alejandro Bellogín, Jun Wang, Pablo Castells
2012 Information retrieval (Boston)  
ranking tasks in collaborative filtering.  ...  A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived  ...  (7) is indeed a user-based collaborative filtering approach, but in a vector space formulation.  ... 
doi:10.1007/s10791-012-9214-z fatcat:z6ujthrrabbqvbfblmmjzvp43i

Large-scale Collaborative Filtering with Product Embeddings [article]

Thom Lake, Sinead A. Williamson, Alexander T. Hawk, Christopher C. Johnson, Benjamin P. Wing
2019 arXiv   pre-print
This paper presents a deep learning based solution to this problem within the collaborative filtering with implicit feedback framework.  ...  Our approach combines neural attention mechanisms, which allow for context dependent weighting of past behavioral signals, with representation learning techniques to produce models which obtain extremely  ...  collaborative filtering models.  ... 
arXiv:1901.04321v1 fatcat:qc7b5qg66zfhvhsq47nsw4hhbu

User Reputation Calculation for Service-Oriented Environments

Paul Rajasingh J, Sharmishtha Sen*, Shreyes Prasad
All the cloud based applications work on serviceoriented architectures and collaborate with multiple components from other services to execute discreet application logic.  ...  A User Reputation model offers a solution to the Service providers in supporting their service decision based on the User Profile.  ...  In low rank matrix factorization based methods, all things are assumed to have a similar low rank space. everything is taken as a vector and the rating that a user provides to an it is the dot product  ... 
doi:10.35940/ijitee.g8953.0510721 fatcat:m64ynid7qnac3m5nue5iqjquhy

Your Click Matters: Enhancing Click-based Image Retrieval performance through Collaborative Filtering

Deepanwita Datta, Manajit Chakraborty, Aveek Biswas
2019 Swiss Text Analytics Conference  
In this paper, we build on this idea and propose a new collaborative filtering based technique to employ the click-log of users from the web to better identify and associate images in response to either  ...  One such feature is the 'click count' based on the clicks an image or its corresponding text gets in response to a query.  ...  Prediction of click count for unseen query Model-based collaborative filter predicts users' rating of unrated items.  ... 
dblp:conf/swisstext/DattaCB19 fatcat:2exzhodujnbenfwwc2cpymkycm

Learning to Rank for Personalized News Article Retrieval

Lorand Dali, Blaz Fortuna, Jan Rupnik
2010 Journal of machine learning research  
The personalized news search ranking model which we have developed takes into account not only document content and metadata, but also data specific to the user such as age, gender, job, income, city,  ...  The focus is on news retrieval and the data from which the ranking model is learned was provided by a large online newspaper.  ...  In the evaluation we compared the personalized ranking model with purely content based ranking models such as BM25 and the vector space model.  ... 
dblp:journals/jmlr/DaliFR10 fatcat:obwycnrvnbht5j7mfz6ogmax5u

Automatic Cell Phone Menu Customization Based on User Operation History

Yusuke Fukazawa, Mirai Hara, Hidetoshi Ueno
2010 Transactions of the Japanese society for artificial intelligence  
Concretely, we define the features of the phone's functions by extracting keywords from the manufacturer's manual, and propose a method that uses the Ranking SVM (Support Vector Machine) to rank the functions  ...  based on user's operation history.  ...  E u,i = x i · w u |x i ||w u | ·3 Ranking Functions using Collaborative Filtering based Ranking SVM (CF-RankingSVM) As the proposed method ranks functions based on the defined feature space, it may fail  ... 
doi:10.1527/tjsai.25.68 fatcat:nmcqdt42dfgwley6vjrsqneehi

Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation [article]

Jarana Manotumruksa, Craig Macdonald, Iadh Ounis
2016 arXiv   pre-print
To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users' existing preferences, and users' contextual  ...  Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare.  ...  In doing so, this paper makes two contributions: (1) we adapt a recent content-based collaborative filtering approach based on word embeddings [13] to effectively identify venues that match the users  ... 
arXiv:1606.07828v1 fatcat:oavpmap6uzgkzb3syunfqgnafm

Knowledge Graph Embedding Based Collaborative Filtering

Yuhang Zhang, Jun Wang, Jie Luo
2020 IEEE Access  
As a result, we propose a Knowledge Graph Embedding based Collaborative Filtering (KGECF) model on the basis of the RotatE model which models a relation as a rotation in complex vector space to ensure  ...  Deep Collaborative Filtering (DeepCF) model is propose by [12] to combine the advantages of representation learning based and matching function learning based collaborative filtering to overcome flaws  ...  The current integration of knowledge graph embedding to collaborative filtering is still preliminary, finding better ways for integrating is also an interesting topic.  ... 
doi:10.1109/access.2020.3011105 fatcat:v2bja3sz2beyngugsvwb6t2j7q

Classified Ranking of Semantic Content Filtered Output Using Self-organizing Neural Networks [chapter]

Marios Angelides, Anastasis Sofokleous, Minaz Parmar
2006 Lecture Notes in Computer Science  
Cosmos-7 is an application that can create and filter MPEG-7 semantic content models with regards to objects and events, both spatially and temporally.  ...  These results are not ranked to the user's ranking of relevancy, which means the user must now laboriously sift through them.  ...  Each user represents a neural network input vector of base vector B. Based on the number of neurons, the neural network will learn to categorize the input vectors it sees.  ... 
doi:10.1007/11840930_6 fatcat:technavewfbd3hg3doj7o5iy44

User Diverse Preference Modeling by Multimodal Attentive Metric Learning

Fan Liu, Zhiyong Cheng, Changchang Sun, Yinglong Wang, Liqiang Nie, Mohan Kankanhalli
2019 Proceedings of the 27th ACM International Conference on Multimedia - MM '19  
by collaborative metric learning.  ...  The obtained attention is then integrated into a metric-based learning method to predict the user preference on this item.  ...  Finally, similar to the previous metric-based collaborative filtering methods [20, 41] , we also bound all the user and item vector within a Euclidean unit sphere, i.e., ∥p * ∥ 2 ≤ 1 and ∥q * ∥ 2 ≤ 1,  ... 
doi:10.1145/3343031.3350953 dblp:conf/mm/LiuCSWNK19 fatcat:3n72o3u7yzcbjobmpui4ts2vfe
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