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A Study of Priors for Relevance-Based
Language Modelling of Recommender Systems
Daniel Valcarce, Javier Parapar, Álvaro Ba...
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A Study of Priors for Relevance-Based Language Modelling of Recommender Systems [RecSys '15 SP Poster]

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Poster for the ACM Recsys 2015 short paper:

Daniel Valcarce, Javier Parapar, Alvaro Barreiro: A Study of Priors for Relevance-Based Language Modelling of Recommender Systems. RecSys 2015: 237-240

http://doi.acm.org/10.1145/2792838.2799677

Published in: Data & Analytics
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A Study of Priors for Relevance-Based Language Modelling of Recommender Systems [RecSys '15 SP Poster]

  1. 1. A Study of Priors for Relevance-Based Language Modelling of Recommender Systems Daniel Valcarce, Javier Parapar, Álvaro Barreiro {daniel.valcarce, javierparapar, barreiro}@udc.es – http://www.irlab.org Information Retrieval Lab, Computer Science Department, University of A Coruña Overview Recently, Relevance-Based Language Models have been adapted to Collaborative Filtering Recommendation [2]. An advantage of this probabilistic modelling is that it naturally introduces the concept of prior probability into the recommendation task. The effect os priors in Language Models has been studied thoroughly in the field of Information Retrieval [1]. In this paper, we study different prior estimates in the context of Collaborative Filtering Recommender Systems. RM for Recommendation 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 via Absolute Discounting smoothing: pδ(i|u) = max(ru,i − δ, 0) + δ |Iu| p(i|C) j∈Iu ru,j • p(i) and p(v) are the item and user prior probabilities estimates Prior estimates Uniform (U) pU (u) = 1 |U| Linear (L) pL(u) = p(u|C) = i∈Iu ru,i v∈U j∈Iv rv,j Probabilistic using Jelinek-Mercer (PJM) pPJM (u) ∝ i∈Iu p(i|u) = (1 − λ) + λ i∈Iu p(i|C) Probabilistic using Dirichlet (PD) pPD(u) ∝ i∈Iu p(i|u) = i∈Iu ru,i + µ i∈Iu p(i|C) µ + i∈Iu ru,i Probabilistic using Absolute Discounting (PAD) pP AD(u) ∝ i∈Iu p(i|u) = i∈Iu max(ru,i − δ, 0) + δ |Iu| i∈Iu p(i|C) j∈Iu ru,j Experiments on the MovieLens 100k collection 0.325 0.330 0.335 0.340 0.345 100 200 300 400 500 600 700 800 900 1000 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 nDCG@10 µ λ, δ U-U L-U PAD(δ)-U PJM(λ)-U PD(µ)-U 0.08 0.10 100 200 300 400 500 600 700 800 900 1000 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 µ nDCG@10 λ, δ U-U L-U L-L L-PAD(δ) L-PJM(µ) L-PD(µ) Values of nDCG@10 for RM2 varying the user prior and taking a Uniform distribution (U) for the item prior. Values of nDCG@10 for RM2 varying the item prior and taking a Linear estimate (L) for the user prior. Other datasets Method MovieLens 100k R3-Yahoo! LibraryThing UB 0.0468bcd 0.0106cd 0.0055b cd SVD 0.0936a cd 0.0103cd 0.0014acd RM2-U-U 0.3296ab d 0.0205ab 0.0900ab d RM2-L-PD (µ = 700) 0.3632abc 0.0207ab 0.0942abc Test values of nDCG@10, bolded cells cor- respond to the best tested method for each dataset. Statistically significant differences with respect to UB, SVD, RM2-U-U and RM2-L-PD are marked with a, b, c and d, respectively. Conclusions • The Linear prior is the best estimate for modelling the user prior probabilities. • The Probabilistic prior based on Dirichlet smooth- ing achieved the highest results as item prior. Bibliography [1] R. Blanco and A. Barreiro. Probabilistic Document Length Priors for Lan- guage Models. In ECIR ’08, pages 394–405, 2008. [2] J. Parapar, A. Bellogín, P. Castells, and A. Barreiro. Relevance-Based Lan- guage Modelling for Recommender Systems. IPM, 49(4):966–980, 2013. RecSys 2015, 9th ACM Conference on Recommender Systems. 16 - 20 September, 2015, Vienna, Austria.

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