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Techniques to Improve Multi-Agent Systems for Searching and Mining the Web
[chapter]
2005
Studies in Computational Intelligence
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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
2022
Mathematical Problems in Engineering
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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
2016
Proceedings of the 1st Workshop on Deep Learning for Recommender Systems - DLRS 2016
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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
2018
International Journal of Engineering & Technology
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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]
2006
Lecture Notes in Computer Science
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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
2019
IJARCCE
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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
2016
Mathematical Problems in Engineering
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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
2004
Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04
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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
2014
ACM transactions on interactive intelligent systems (TiiS)
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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
2015
Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15
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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]
2017
arXiv
pre-print
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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
2013
International Journal of Computer Applications
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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
2017
Proceedings of the 26th International Conference on World Wide Web - WWW '17
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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]
2013
Proceedings of the 2013 SIAM International Conference on Data Mining
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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]
2016
arXiv
pre-print
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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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