NDLI: Learning to rank recommendations with the k-order statistic loss

Please wait, while we are loading the content...

Context-aware review helpfulness rating prediction

Orthogonal query recommendation

Diffusion-aware personalized social update recommendation

Personalized news recommendation with context trees

Topic diversity in tag recommendation

Exploiting non-content preference attributes through hybrid recommendation method

Evaluation of recommendations: rating-prediction and ranking

A fast parallel SGD for matrix factorization in shared memory systems

Catch-up TV recommendations: show old favourites and find new ones

A people-to-people content-based reciprocal recommender using hidden markov models

Dynamic generation of personalized hybrid recommender systems

GAIN: web service for user tracking and preference learning - a smart TV use case

The fifth ACM RecSys workshop on recommender systems and the social web

Recommendation in social networks

Query-driven context aware recommendation

Understanding and improving relational matrix factorization in recommender systems

Recommending branded products from social media

What to read next?: making personalized book recommendations for K-12 users

Rating support interfaces to improve user experience and recommender accuracy

Hybrid event recommendation using linked data and user diversity

You are what you consume: a bayesian method for personalized recommendations

DrunkardMob: billions of random walks on just a PC

Generating supplemental content information using virtual profiles

Acquiring user profiles from implicit feedback in a conversational recommender system

Accuracy and robustness impacts of power user attacks on collaborative recommender systems

PEN RecSys: a personalized news recommender systems framework

Workshop on human decision making in recommender systems: decisions@RecSys'13

Learning to rank for recommender systems

Location-aware music recommendation using auto-tagging and hybrid matching

Retargeted matrix factorization for collaborative filtering

Top-N recommendations from implicit feedback leveraging linked open data

Movie recommender system for profit maximization

ReComment: towards critiquing-based recommendation with speech interaction

Pairwise learning in recommendation: experiments with community recommendation on linkedin

To personalize or not: a risk management perspective

Using maximum coverage to optimize recommendation systems in e-commerce

A system for advice provision in multiple prospectselection problems

Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems

A heterogeneous graph-based recommendation simulator

Workshop and challenge on news recommender systems

Beyond friendship: the art, science and applications of recommending people to people in social networks

Spatial topic modeling in online social media for location recommendation

Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach

Exploring temporal effects for location recommendation on location-based social networks

Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection

Hidden factors and hidden topics: understanding rating dimensions with review text

Which app will you use next?: collaborative filtering with interactional context

Online multi-task collaborative filtering for on-the-fly recommender systems

Efficient top-n recommendation for very large scale binary rated datasets

Clustering-based factorized collaborative filtering

Agent-based computational investing recommender system

Design and evaluation of a client-side recommender system

Workshop on recommender systems meet big data & semantic technologies: SeRSy 2013

Tutorial on preference handling

Nonlinear latent factorization by embedding multiple user interests

The curated web: a recommendation challenge

Personalized next-song recommendation in online karaokes

Improving augmented reality using recommender systems

A food recommender for patients in a care facility

Learning to rank recommendations with the k-order statistic loss

Distributed matrix factorization with mapreduce using a series of broadcast-joins

Cross social networks interests predictions based ongraph features

Beyond rating prediction accuracy: on new perspectives in recommender systems

Sage: recommender engine as a cloud service

Workshop on reproducibility and replication in recommender systems evaluation: RepSys

Differential data analysis for recommender systems

Anytime algorithms for top-N recommenders

First workshop on large-scale recommender systems: research and best practice(LSRS 2013)

Effectiveness of the data generated on different time in latent factor model

RecSys challenge 2013

Interview process learning for top-n recommendation

Escape the bubble: guided exploration of music preferences for serendipity and novelty

Evaluating top-n recommendations "when the best are gone"

An analysis of tag-recommender evaluation procedures

Recommendation in heterogeneous information networks with implicit user feedback

Recommendation opportunities: improving item prediction using weighted percentile methods in collaborative filtering systems

Improving user profile with personality traits predicted from social media content

Leveraging the citation graph to recommend keywords

Local context modeling with semantic pre-filtering

Musical recommendations and personalization in a social network

Not by search alone: how recommendations complement search results

OFF-set: one-pass factorization of feature sets for online recommendation in persistent cold start settings

Personalised ranking with diversity

Prior ratings: a new information source for recommender systems in e-commerce

Probabilistic collaborative filtering with negative cross entropy

Recommending improved configurations for complex objects with an application in travel planning

Recommending patents based on latent topics

Recommending scientific articles using bi-relational graph-based iterative RWR

Sample selection for MCMC-based recommender systems

Selecting content-based features for collaborative filtering recommenders

Sentimental product recommendation

Set-oriented personalized ranking for diversified top-n recommendation

Towards scalable and accurate item-oriented recommendations

Using geospatial metadata to boost collaborative filtering

When power users attack: assessing impacts in collaborative recommender systems

xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance

Evolving friend lists in social networks

Exploratory and interactive daily deals recommendation

Learning to rank recommendations with the k-order statistic loss

Content Provider	ACM Digital Library
Author	Yee, Hector Weiss, Ron J. Weston, Jason
Abstract	Making recommendations by learning to rank is becoming an increasingly studied area. Approaches that use stochastic gradient descent scale well to large collaborative filtering datasets, and it has been shown how to approximately optimize the mean rank, or more recently the top of the ranked list. In this work we present a family of loss functions, the k-order statistic loss, that includes these previous approaches as special cases, and also derives new ones that we show to be useful. In particular, we present (i) a new variant that more accurately optimizes precision at k, and (ii) a novel procedure of optimizing the mean maximum rank, which we hypothesize is useful to more accurately cover all of the user's tastes. The general approach works by sampling N positive items, ordering them by the score assigned by the model, and then weighting the example as a function of this ordered set. Our approach is studied in two real-world systems, Google Music and YouTube video recommendations, where we obtain improvements for computable metrics, and in the YouTube case, increased user click through and watch duration when deployed live on www.youtube.com.
Starting Page	245
Ending Page	248
Page Count	4
File Format	PDF
ISBN	9781450324090
DOI	10.1145/2507157.2507210
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Loss functions Collaborative filtering Learning to rank Matrix factorization Stochastic gradient
Content Type	Text
Resource Type	Article

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in