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Techniques to Improve Multi-Agent Systems for Searching and Mining the Web [chapter]

E. Herrera-Viedma, C. Porcel, F. Herrera, L. Martínez, A.G. Lopez-Herrera
2005 Studies in Computational Intelligence  
Web multi-agent systems assist the users by gathering from Internet the information that best satisfies their specific needs.  ...  The information gathering in Internet is a complex activity and Internet users need tools to assist them to find the information required.  ...  Finally, informs the collaborative filtering agent on set of documents used by user to satisfy his/her information needs DU. • Level 3: Collaborative filtering agent (one for interface agent), that communicates  ... 
doi:10.1007/11004011_23 fatcat:vm6lymkpurd4lft63okl3ugcyq

Deep Collaborative Filtering: A Recommendation Method for Crowdfunding Project Based on the Integration of Deep Neural Network and Collaborative Filtering

Pei Yin, Jing Wang, Jun Zhao, Huan Wang, Hongcheng Gan, Wei Liu
2022 Mathematical Problems in Engineering  
collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation.  ...  In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm.  ...  [10] divided collaborative filtering recommendation algorithms into two main categories: one is memory-based collaborative filtering recommendation, and the other is model-based collaborative filtering  ... 
doi:10.1155/2022/4655030 fatcat:6nadmi32g5hrzaurn3bukqazzi

Neural Autoregressive Collaborative Filtering for Implicit Feedback

Yin Zheng, Cailiang Liu, Bangsheng Tang, Hanning Zhou
2016 Proceedings of the 1st Workshop on Deep Learning for Recommender Systems - DLRS 2016  
This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback ( e.g. click, watch, browse behaviors).  ...  We first convert a users implicit feedback into a like vector and a confidence vector, and then model the probability of the like vector, weighted by the confidence vector.  ...  Experimental results on three real-world benchmark datasets show that CF-NADE outperforms the state-of-theart methods on collaborative filtering tasks. all results of this work rely on explicit feedback  ... 
doi:10.1145/2988450.2988453 dblp:conf/recsys/ZhengLTZ16 fatcat:m6ig6pyygfgtlb4jot5wd2ccda

The Design of Web Based Car Recommendation System using Hybrid Recommender Algorithm

Leyo Babu Thomas, V Vaidhehi
2018 International Journal of Engineering & Technology  
The proposed hybrid recommender algorithm is the combination of user-to-user and item-to-item collaborative filtering method to generate the car recommendations.  ...  The synthetic dataset of 300 users with 10000 sessions is used to build user model.  ...  This paper suggests the design of hybrid recommendation system using item-item collaborative filtering, user-user collaborative filtering and matching with user constraints.  ... 
doi:10.14419/ijet.v7i3.4.16772 fatcat:qhmj74zfincenkaaaijuv6aujy

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.  ...  In related works neural network have been applied to collaborative filtering to cluster users into groups based on similar tastes [11] .  ... 
doi:10.1007/11840930_6 fatcat:technavewfbd3hg3doj7o5iy44

Hybrid Recommender System for Therapy Recommendation

V Vishwajith, S Kaviraj, R Vasanth
2019 IJARCCE  
The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality.  ...  Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, were proposed.  ...  Recommendation Using Collaborative Filtering Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information  ... 
doi:10.17148/ijarcce.2019.8118 fatcat:r2thundp6jhjtoedx3w2w24v6q

Exploiting Explicit and Implicit Feedback for Personalized Ranking

Gai Li, Qiang Chen
2016 Mathematical Problems in Engineering  
The problem of the previous researches on personalized ranking is that they focused on either explicit feedback data or implicit feedback data rather than making full use of the information in the dataset  ...  Until now, nobody has studied personalized ranking algorithm by exploiting both explicit and implicit feedback.  ...  The research on implicit feedback about CF is also called One-Class Collaborative Filtering (OCCF) [8] [9] [10] [11] [12] [13] [14] [15] , in which only positive implicit feedback or only positive examples  ... 
doi:10.1155/2016/2535329 fatcat:o7l3meksmbd4tbsaejgyzz2zoa

TiVo

Kamal Ali, Wijnand van Stam
2004 Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04  
TiVo uses an item-item (show to show) form of collaborative filtering which obviates the need to keep any persistent memory of each user s viewing preferences at the TiVo server.  ...  We describe the TiVo television show collaborative recommendation system which has been fielded in over one million TiVo clients for four years.  ...  Thanks to Mike Pazzani and Cliff Brunk for feedback..  ... 
doi:10.1145/1014052.1014097 dblp:conf/kdd/AliS04 fatcat:ofqkxxgvvjcg7e3ffz2adr6cdu

Modeling User Preferences in Recommender Systems

Gawesh Jawaheer, Peter Weller, Patty Kostkova
2014 ACM transactions on interactive intelligent systems (TiiS)  
Finally, we formulate challenges for future research on improvement of user feedback.  ...  In this article, we propose a classification framework for the use of explicit and implicit user feedback in recommender systems based on a set of distinct properties that include Cognitive Effort, User  ...  In addition, most of research reviewed falls into collaborative filtering-based recommender systems.  ... 
doi:10.1145/2512208 fatcat:vykieckuybdzpo4rd4wnxwoyga

Collaborative filtering with recency-based negative feedback

João Vinagre, Alípio Mário Jorge, João Gama
2015 Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15  
One particular challenge of positive-only data is how to interpret absent user-item interactions. These can either be seen as negative or as unknown preferences.  ...  Instead, data consists of positive-only user-item interactions, and the task is therefore not to predict ratings, but rather to predict good items to recommend -item prediction.  ...  This problem is also known as One-Class Collaborative Filtering (OCCF), given its similarity to One-class Classification [3] .  ... 
doi:10.1145/2695664.2695998 dblp:conf/sac/VinagreJG15 fatcat:brby3hnshjf7ld2b3jgqgdj7o4

Neural Collaborative Filtering [article]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
2017 arXiv   pre-print
In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback.  ...  When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent  ...  Acknowledgement The authors thank the anonymous reviewers for their valuable comments, which are beneficial to the authors' thoughts on recommendation systems and the revision of the paper.  ... 
arXiv:1708.05031v2 fatcat:gam2aezz2retvlf2cqqrqv7oni

A Review on Recommender System

L. Anitha, M. Kavitha Devi, P. Anjali Devi
2013 International Journal of Computer Applications  
users.  ...  Recommender System applies various Data Mining methodologies to recommend efficiently for all active users based on their interest, preferences and ratings given for previous items and even based on similar  ...  Amazon works on item-item collaborative filtering in which similarity between items are found instead of finding similarity between users.  ... 
doi:10.5120/14098-2115 fatcat:u4l3kzg56rb53otv5jqoem2imq

Neural Collaborative Filtering

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -collaborative filtering -on the basis of implicit feedback.  ...  When it comes to model the key factor in collaborative filtering -the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent  ...  Acknowledgement The authors thank the anonymous reviewers for their valuable comments, which are beneficial to the authors' thoughts on recommendation systems and the revision of the paper.  ... 
doi:10.1145/3038912.3052569 dblp:conf/www/HeLZNHC17 fatcat:sb4tvd5e4jexblmbqcspzyomyq

CoFiSet: Collaborative Filtering via Learning Pairwise Preferences over Item-sets [chapter]

Weike Pan, Li Chen
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
Collaborative filtering aims to make use of users' feedbacks to improve the recommendation performance, which has been deployed in various industry recommender systems.  ...  With this assumption, we further develop a general algorithm called CoFiSet (collaborative filtering via learning pairwise preferences over item-sets).  ...  One fundamental challenge in collaborative filtering with implicit feedbacks is the lack of negative feedbacks.  ... 
doi:10.1137/1.9781611972832.20 dblp:conf/sdm/ChenP13 fatcat:pwpbtpcwnzcmnfpqk5uewauti4

A Neural Autoregressive Approach to Collaborative Filtering [article]

Yin Zheng, Bangsheng Tang, Wenkui Ding, Hanning Zhou
2016 arXiv   pre-print
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive  ...  Furthermore, we take the ordinal nature of the preferences into consideration and propose an ordinal cost to optimize CF-NADE, which shows superior performance.  ...  Experimental results on three real-world benchmark datasets show that CF-NADE outperforms the state-of-theart methods on collaborative filtering tasks. all results of this work rely on explicit feedback  ... 
arXiv:1605.09477v1 fatcat:jbsvlqt2x5fhpaekr6sq3avvky
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