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Filters
TopRec
2013
Proceedings of the 22nd international conference on World Wide Web - WWW '13
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In this paper, we propose a unified framework, TopRec, which detects topical communities to construct interpretable domains for domain-specific collaborative filtering. ...
Traditionally, Collaborative Filtering assumes that similar users have similar responses to similar items. ...
Memory-based CF algorithm usually search for the similar users or items to produce a prediction or top-n recommendation [22, 9] . ...
doi:10.1145/2488388.2488519
dblp:conf/www/ZhangCYNL13
fatcat:ybcj3rfxzjbrddn5yvepvoscwy
TopRecs + : Pushing the Envelope on Recommender Systems
2011
IEEE Data Engineering Bulletin
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We follow item-based collaborative filtering (CF) [4] , which is used widely in academia and practice [14, 5] . ...
Rating Matrix
Item Recommendation Process
Item Metadata
Package Recommendation Process
Users
Similarity Matrix
Top-k Item Recommendation Algorithm
Top-k Items Top-k Packages
TopRecs + Figure ...
dblp:journals/debu/KhabbazXL11
fatcat:5o2cciyusvgnll6m6rvgtdntma
Fast Group Recommendations by Applying User Clustering
[chapter]
2012
Lecture Notes in Computer Science
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We efficiently aggregate the single user recommendations into group recommendations by leveraging the power of a top-k algorithm. We evaluate our approach in a real dataset of movie ratings. ...
However, there are contexts in which the items to be suggested are not intended for a single user but for a group of people. ...
The k most prominent items for the group are identified by exploiting a top-k algorithm. ...
doi:10.1007/978-3-642-34002-4_10
fatcat:mxyaxzitp5fhnp34nmdxoc4e3q
Fifty Shades of Ratings
2016
Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16
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2016 pre-print arXiv:1607.04228v1 fatcat:ykz72vwsejehdpd2osqvco67gq
Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. ...
Due to that bias, standard algorithms, as well as commonly used evaluation metrics, become insensitive to negative feedback. ...
Conventional collaborative filtering algorithms, such as matrix factorization or similarity-based models, tend to favor similar items, which are likely to be irrelevant in that case. ...
doi:10.1145/2959100.2959170
dblp:conf/recsys/FrolovO16
fatcat:fepqjkbgszh75p2sao5iacutei
A Survey of Recommender System from Data Sources Perspective
2018
Proceedings of the 2018 8th International Conference on Management, Education and Information (MEICI 2018)
unpublished
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Collaborative filtering, as a classical algorithm, has become the basis of the recommender system. ...
In recent years, there are more and more recommender systems based on multiple data sources are proposed. ...
Acknowledgements Supported by the Fundamental Research Funds for the Central Universities (2017YJS215). ...
doi:10.2991/meici-18.2018.2
fatcat:sltkgknckfhqbosx56yyml3nze