exploring statistical language models for recommender systems [recsys '15 ds poster]

Exploring Statistical Language Models for Recommender Systems Daniel Valcarce [email protected] – http://www.irlab.org Information Retrieval Lab, Computer Science Department, University of A Coruña Information Retrieval (IR) Goal Retrieve relevant documents according to the information need of a user. Examples Search engines. Methods They can be based on: Vector Vector Space Model. Matrix factorisation Latent Semantic Indexing. Probabilistic modelling Language Models. Information Fitering (IF) Goal Select relevant items from an information stream for a given user. Examples spam filters, recommender systems. Methods Some Collaborative Filtering methods are: Vector Pairwise similarities (cosine, Pearson, etc.). Matrix factorisation SVD, NMF. Probabilistic modelling LDA. Overview • Information Filtering (IF) and Information Retrieval (IR) are two sibling fields. • Statistical Language Models are a successful technique in IR → Explore how to apply them to recommendation. • We start by improving the current adaptation of Relevance-Based Language Models to Collaborative Filtering [1]. Relevance-Based Language Models IR RecSys Query Target user Document Neighbour Term Item RM2 : p(i|R u ) ∝ p(i) Y j ∈I u X v ∈V u p(i|v ) p(v ) p(i) p(j |v ) • I u is the set of items rated by the user u. • V u is the set of neighbours of the user u. • p(i|u) is computed smoothing the maximum likelihood es- timate. • p(i) and p(v ) are the item and user priors. Smoothing methods Smoothing deals with data sparsity and plays a similar role to the IDF using a background model: p(i|C )= ∑ v ∈U r v,i ∑ j ∈I ,v ∈U r v,j [3]. Jelinek-Mercer (JM) p λ (i|u) = (1 - λ) r u,i ∑ j ∈I u r u,j + λp(i|C ) Dirichlet Priors (DP) p μ (i|u)= r u,i + μp(i|C ) μ + ∑ j ∈I u r u,j Absolute Discounting (AD) p δ (i|u)= max(r u,i - δ, 0) + δ |I u | p(i|C ) ∑ j ∈I u r u,j Priors Priors provide a principled way of introducing knowledge into the recommender [2]. Uniform (U) Linear (L) User prior p U (u)= 1 |U| p L (u)= ∑ i∈I u r u,i ∑ v ∈U ∑ j ∈I v r v,j Item prior p U (i)= 1 |I| p L (i)= ∑ u∈U i r u,i ∑ j ∈I ∑ v ∈U j r v,j Experiments on MovieLens 100k Algorithm nDCG@10 Gini@10 MSI@10 SVD 0.0946 0.0109 14.6129 SVD++ 0.1113 0.0126 14.9574 NNCosNgbr 0.1771 0.0344 16.8222 UIR-Item 0.2188 0.0124 5.2337 PureSVD 0.3595 0.1364 11.8841 RM2-JM 0.3175 0.0232 9.1087 RM2-DP 0.3274 0.0251 9.2181 RM2-AD 0.3296 0.0256 9.2409 RM2-AD-L-U 0.3423 0.0264 9.2004 Research directions • Some techniques developed for solving IR problems can be effectively applied to recommendation. • Probabilistic models from IR are competitive recommendation algorithms although there is still room for improvements. • Language Models provide an interpretable and principled way of generate recommendations. • Using different priors [2] or clustering algorithms for the neighbourhoods [1] can improve RM2. • We envision as future work the development of context-aware and hybrid recommendations under the Language Modelling. Bibliography [1] J. Parapar, A. Bellogín, P. Castells, and A. Bar- reiro. Relevance-Based Language Modelling for Recom- mender Systems. Information Processing & Management, 49(4):966–980, 2013. [2] D. Valcarce, J. Parapar, and A. Barreiro. A Study of Priors for Relevance-Based Language Modelling of Recommender Systems. In RecSys ’15. ACM, 2015. [3] D. Valcarce, J. Parapar, and A. Barreiro. A Study of Smoothing Methods for Relevance-Based Language Mod- elling of Recommender Systems. In ECIR ’15, volume 9022, pages 346–351. Springer, 2015. RecSys 2015, 9th ACM Conference on Recommender Systems. 16 - 20 September, 2015, Vienna, Austria.

Upload: daniel-valcarce

Post on 18-Feb-2017

82 views

Category:

Data & Analytics

1 download

Report

Download

Embed Size (px):

TRANSCRIPT

Page 1: Exploring Statistical Language Models for Recommender Systems [RecSys '15 DS Poster]

Exploring Statistical Language Modelsfor Recommender SystemsDaniel [email protected] – http://www.irlab.orgInformation Retrieval Lab, Computer Science Department, University of A Coruña

Information Retrieval (IR)Goal Retrieve relevant documents according to the information

need of a user.Examples Search engines.Methods They can be based on:

Vector Vector Space Model.Matrix factorisation Latent Semantic Indexing.Probabilistic modelling Language Models.

Information Fitering (IF)Goal Select relevant items from an information stream for a

given user.Examples spam filters, recommender systems.Methods Some Collaborative Filtering methods are:

Vector Pairwise similarities (cosine, Pearson, etc.).Matrix factorisation SVD, NMF.Probabilistic modelling LDA.

Overview• Information Filtering (IF) and Information Retrieval (IR) are two sibling fields.• Statistical Language Models are a successful technique in IR → Explore how to apply them to recommendation.• We start by improving the current adaptation of Relevance-Based Language Models to Collaborative Filtering [1].

Relevance-Based Language Models

IR RecSys

Query Target userDocument Neighbour

Term Item

RM2 : p(i|Ru) ∝ p(i)∏j∈Iu

∑v∈Vu

p(i|v) p(v)p(i)

p(j|v)

• Iu is the set of items rated by the user u.• Vu is the set of neighbours of the user u.• p(i|u) is computed smoothing the maximum likelihood es-timate.• p(i) and p(v) are the item and user priors.

Smoothing methodsSmoothing deals with data sparsity and plays a similar role tothe IDF using a background model: p(i|C) =

∑v∈U rv,i∑

j∈I, v∈U rv,j[3].

Jelinek-Mercer(JM)

pλ(i|u) = (1− λ) ru,i∑j∈Iu ru,j

+ λ p(i|C)

Dirichlet Priors(DP)

pµ(i|u) =ru,i + µ p(i|C)µ+

∑j∈Iu ru,j

AbsoluteDiscounting

(AD)pδ(i|u) =

max(ru,i − δ, 0) + δ |Iu| p(i|C)∑j∈Iu ru,j

PriorsPriors provide a principled way of introducing knowledge intothe recommender [2].

Uniform (U) Linear (L)

Userprior

pU(u) =1

|U| pL(u) =

∑i∈Iu ru,i∑

v∈U∑

j∈Iv rv,j

Itemprior

pU(i) =1

|I| pL(i) =

∑u∈Ui

ru,i∑j∈I

∑v∈Uj

rv,j

Experiments on MovieLens 100k

Algorithm nDCG@10 Gini@10 MSI@10

SVD 0.0946 0.0109 14.6129SVD++ 0.1113 0.0126 14.9574NNCosNgbr 0.1771 0.0344 16.8222UIR-Item 0.2188 0.0124 5.2337PureSVD 0.3595 0.1364 11.8841RM2-JM 0.3175 0.0232 9.1087RM2-DP 0.3274 0.0251 9.2181RM2-AD 0.3296 0.0256 9.2409RM2-AD-L-U 0.3423 0.0264 9.2004

Research directions• Some techniques developed for solving IR problemscan be effectively applied to recommendation.• Probabilisticmodels from IR are competitive recom-mendation algorithms although there is still room forimprovements.• Language Models provide an interpretable and prin-cipled way of generate recommendations.• Using different priors [2] or clustering algorithms forthe neighbourhoods [1] can improve RM2.• We envision as future work the development ofcontext-aware and hybrid recommendations underthe Language Modelling.

Bibliography[1] J. Parapar, A. Bellogín, P. Castells, and A. Bar-

reiro. Relevance-Based Language Modelling for Recom-mender Systems. Information Processing & Management,49(4):966–980, 2013.

[2] D. Valcarce, J. Parapar, and A. Barreiro. A Study of Priorsfor Relevance-Based Language Modelling of RecommenderSystems. In RecSys ’15. ACM, 2015.

[3] D. Valcarce, J. Parapar, and A. Barreiro. A Study ofSmoothing Methods for Relevance-Based Language Mod-elling of Recommender Systems. In ECIR ’15, volume 9022,pages 346–351. Springer, 2015.

RecSys 2015, 9th ACM Conference on Recommender Systems. 16 - 20 September, 2015, Vienna, Austria.

Recommender Systems Evaluation: A 3D Benchmark - presented at RUE 2012 workshop at ACM Recsys 2012

Deep Learning for Recommender Systems - Budapest RecSys Meetup

Recsys virtual-profiles

SANMI KOYEJO CS @ ILLINOIS€¦ · recommender algorithms on top-n recommendation tasks." Recsys, 2010. Metric choice has a large impact on real- ... Fit a logistic regression model

Intro to RecSys and CCF Brian Ackerman 1. Roadmap Introduction to Recommender Systems & Collaborative Filtering Collaborative Competitive Filtering 2

Recsys 2014 Tutorial - The Recommender Problem Revisited

Ordinal Random Fields for Recommender Systemsproceedings.mlr.press/v39/liu14.pdfRecommender Systems (RecSys) aim to suggest items that are potentially of interest to users, where the

Recommendation Architecture - OpenTable - RecSys 2014 - Large Scale Recommender Systems

Personalized Entity Recommendation: A Heterogeneous ...€¦ · Yuan, RecSys’11) 3 . The Heterogeneous Information Network View of Recommender System 4 Avatar Aliens Titanic Revolution-ary

TrailMix: An Ensemble Recommender System for Playlist ...faculty.cs.tamu.edu/caverlee/pubs/zhao18recsyschallenge.pdfTrailMix: An Ensemble Recommender System for Playlists RecSys Challenge

Bolzano, IT Recsys 2017 Summer School Industry Quora/Xavier …pro.unibz.it/projects/schoolrecsys17/RecsysSummerSchool-XavierAmatriain.pdf · 1. The Traditional Recommender Problem