Poster for the Doctoral Symposium paper in ACM RecSys 2015: Daniel Valcarce: Exploring Statistical Language Models for Recommender Systems. RecSys 2015: 375-378 http://doi.acm.org/10.1145/2792838.2796547

1. Exploring Statistical Language Models
for Recommender Systems
Daniel Valcarce
daniel.valcarce@udc.es – 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|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 methods
Smoothing deals with data sparsity and plays a similar role to
the 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
Absolute
Discounting
(AD)
pδ(i|u) =
max(ru,i − δ, 0) + δ |Iu| p(i|C)
j∈Iu
ru,j
Priors
Priors provide a principled way of introducing knowledge into
the recommender [2].
Uniform (U) Linear (L)
User
prior
pU (u) =
1
|U|
pL(u) = i∈Iu
ru,i
v∈U j∈Iv
rv,j
Item
prior
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 14.6129
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 recom-
mendation algorithms although there is still room for
improvements.
• Language Models provide an interpretable and prin-
cipled 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.