PocketLens: Toward a personal recommender system

By Bradley N. Miller, J. Konstan, John Riedl. TOIS, 2004

369 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/112ecdbc76916eddf0878b069473abc9a7763f42

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

Citations in the corpus

Collaborative Filtering Recommender Systems (2011, methodology): “Other matrix factorization techniques such as singular value decomposition have also been deployed to implement decentralized recommenders: the PocketLens algorithm used SVD with encryption to perform decentralized collaborative filtering on hand-held devices [101] and Polat and Du [113] proposed us”
Want to scale in centralized systems? Think P2P (2015, methodology): “Similarly, PocketLens [52] is a decentralized recommender algorithm developed by the GroupeLens research group.”
Preventing shilling attacks in online recommender systems (2005, methodology): “Building upon these results, we propose and evaluate an algorithm for protecting recommender systems against shilling attacks.”
A collaborative filtering approach to mitigate the new user cold start problem (2012, background): “Movie recommendation websites are probably the best-known cases to users and are without a doubt the most thoroughly studied by researchers [25,2], although there are many other fields in which RS have great and increasing importance, such as e-commerce [23,57,52,36], e-learning [12,4,21], music [15”
Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs (2011, background): “This concern can be eased by developing personal recommenders that store personal information locally or only share it in encrypted form [27].”
A Survey of Collaborative Filtering Techniques (2009, background): “The ratings can either be explicit indications, and so forth, on a 1–5 scale, or implicit indications, such as purchases or click-throughs [4].”
A Distributed, Architecture-Centric Approach to Computing Accurate Recommendations from Very Large and Sparse Datasets (2011, methodology): “Distributed recommender systems usually employ smart clients to process and store recommendation data [105, 165, 166, 169].”
Federating Recommendations Using Differentially Private Prototypes (2020, positive): “A number of private recommender systems have been developed using a cryptographic approach (Miller et al., 2004; Kobsa and Schreck, 2003).”
Recommender system application developments: A survey (2015, methodology): “ACR News e-resource CB, Clustering Web-based Individual 2000 [133] ArgueNet e-resource CB Web-based Individual 2004 [145] PocketLens e-resource CF, model-based Web-based Individual 2004 [146]”
Generic User Modeling Systems (2001, background): “be truncated substantially without much loss in quality [67].”
A new collaborative filtering metric that improves the behavior of recommender systems (2010, background): “Recommender systems cover a wide variety of applications [20,4,10,28,5,14,27], although those related to movie recommendations are the most well-known and most widely-used in the research field [23,3,25].”
WHATSUP: A Decentralized Instant News Recommender (2013, background): “Decentralized recommenders: Research on decentralized recommender systems is still modest despite their clear scalability advantage [26], [27], [11].”