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Exploring Statistical Language Models
for Recommender Systems
Daniel Valcarce
daniel.valcarce@udc.es – http://www.irlab.or...
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Exploring Statistical Language Models for Recommender Systems [RecSys '15 DS Poster]

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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

Published in: Data & Analytics
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Exploring Statistical Language Models for Recommender Systems [RecSys '15 DS Poster]

  1. 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.

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