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Towards Improving Top-N Recommendation by Generalization of SLIM

Santiago Larraín, Denis Parra, Alvaro Soto

Computer Science Department Pontificia Universidad Católica de Chile

Santiago, Chile

slarrain@uc.cl,dparras@uc.cl,asoto@ing.puc.cl

ABSTRACT

Sparse Linear Methods (SLIM) are state-of-the-art recommenda- tion approaches based on matrix factorization, which rely on a reg- ularized1-norm and2-norm optimization –an alternative opti- mization problem to the traditional Frobenious norm. Although they have shown outstanding performance in Top-N recommenda- tion, existent works have not yet analyzed some inherent assump- tions that can have an important effect on the performance of these algorithms. In this paper, we attempt to improve the performance of SLIM by proposing a generalized formulation of the aforemen- tioned assumptions. Instead of directly learning a sparse represen- tation of the user-item matrix, we (i) learn the latent factors’ matrix of the users and the items via a traditional matrix factorization ap- proach, and then (ii) reconstruct the latent user or item matrix via prototypes which are learned using sparse coding, an alternative SLIM commonly used in the image processing domain. The re- sults show that by tuning the parameters of our generalized model we are able to outperform SLIM in several Top-N recommendation experiments conducted on two different datasets, using both nDCG and nDCG@10 as evaluation metrics. These preliminary results, although not conclusive, indicate a promising line of research to improve the performance of SLIM recommendation.

Categories and Subject Descriptors

H.4 [Information Systems Applications]: Miscellaneous; H.3.3 [Information Search and Retrieval]: Information filtering

Keywords

Recommender Systems, matrix factorization, SLIM

1. INTRODUCTION

The main goal of a recommender system is helping users dealing with information overload by providing personalized suggestions.

This so-called “recommendation problem” has been addressed in different ways, such as predicting unobserved user ratings or as Top-N recommendation [2], where the objective is to optimize the ranking of the N most relevant items, to eventually recommend

Copyright is held by the author(s).

RecSys 2015Poster Proceedings, September 16-20, Vienna, Austria.

.

them. Under the paradigm of Top-N recommendation, Sparse Lin- ear Methods (SLIM) have shown an outstanding performance over traditional matrix factorization algorithms, motivating the creation of extensions [7, 8] to the original work of Ning and Karypis [6].

If we compare SLIM with other similar applications that use1- norm and 2-norm optimization, such as sparse coding used for dictionary learning in image processing, we can identify two as- sumptions. First, SLIM aims at reconstructing directly the low- density user-item matrixA, but low-density datasets decrease the effectiveness of Top-N recommendations [1]. We hypothesize that re-constructing a denser representation of the user-item interactions might produce an improvement in recommendation performance.

Secondly, SLIM does not require to learn a dictionary of proto- types to reconstruct the matrixA, while dictionary learning is one of the most important stages of the matrix reconstruction in sparse coding. Our intuition is that by testing these two assumptions we might improve the ranking performance of SLIM. Hence, we can summarize the hypotheses for the generalized SLIM (gSLIM) that motivates our work in two: (1) in a first stage, learning a denser representation of the user-interaction matrix, such as the the low- rank users’ and items’ latent factor matrices, can improve the final Top-N recommendation performance, and (2) learning a dictionary of prototypes when reconstructing the low-rank user or item la- tent matrices can improve the final Top-N recommendation perfor- mance.

In this document, we explain our gSLIM approach, preliminary experiments on two datasets showing promising results, and finally we discuss limitations and some interesting ideas for future work.

2. GSLIM RECOMMENDATION MODEL

In order to model each user we use a traditional regularized ma- trix factorization [3] technique as defined in equation 1 whereA RU×I represents our rating matrix ofU users andI items. U Rk1×U represents the latent users’ matrix where each user⃗ui is represented withk1latent dimensions, andVRk1×Irepresents the latent items’ matrix where each item⃗viis represented withk1

latent dimensions as well.

min

U,V ||AUTV||2F+λ1(||U||2F+||V||2F) (1) After learning the latent users’ matrixU, we proceed to compute the latent social prototypes. Similar as Karypis et. al. [6] we recon- struct each user⃗uias a sparse lineal combination of our prototype matrixP Rk1×k2. In equation 2 we introduce a sparse coding problem [4] in order to compute our prototype matrixP, wherek2

is the number of prototypes. The matrixW Rk2×Urepresents the sparse coding coefficients andλ2is the sparsity parameter.

minP,W ||UPW||2F+λ2||W||1, s.t. ||wi||= 1 i (2)

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Algorithm ML-100K ML-1M

nDCG nDCG@10 nDCG nDCG@10

k1= 30,k2= 200,γ= 9 0.9357 0.3043 - - k1= 20,k2= 943,γ= 5 - - 0.9398 0.2152 k1= 20,k2= 200,γ= 10 - - 0.9392 0.2155

SLIM (baseline) 0.9202 0.2927 0.9230 0.2374

Traditional MF 0.9361 0.3068 0.9377 0.2111

Table 1: Results of nDCG and nDCG@10 for ML datasets.

Finally to make a recommendationrˆi,jfor the userion the item jwe multiply vectorsrˆi,j=u⃗Ti ·⃗vj, where user’s reconstruction defined by equation 3 is solved by an Orthogonal Matching Pursuit (OMP) algorithm with a maximum ofγnon-negative coefficients for coefficientsw.⃗

ui=Pargmin

w (⃗uiPw) (3)

gSLIM compared to SLIM. Unlike our method, SLIM [6] solves directly a similar problem to equation 2, but instead of the latent users’ matrix U, it reconstructs the user-item matrix Awithout learning the prototypesP, as shown in equation 4.

minW

1

2||AAW||2F+β

2||W||2F+λ||W||1,s.t.W0diag(W) = 0

3. EXPERIMENTS

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Using theMovielensdatasets with 100K (ML-100K,|u|= 943,

|i|= 1682) and 1M (ML-1M,|u|= 6040,|i|= 3952) ratings1, we performed top-N recommendation experiments by using the LensKit framework2, evaluating the performance with nDCG and nDCG@10 metrics [5]. We compared SLIM3, a regularized matrix factorization (MF) as in equation 1, and gSLIM. For gSLIM we also conducted a parameter analysis: 1)k1, latent dimensions for U, 2)k2, amount of different social prototypes, and 3)γ, number of non-negative coefficients on the user’s sparse reconstruction. All experiments where conducted using 5-fold cross validation. The parametersλ1andλ2where set to0.01and1respectively.

gamma: 1 gamma: 3 gamma: 5 gamma: 7 gamma: 9 gamma: 10

0.920 0.925 0.930 0.935

0.920 0.925 0.930 0.935

0.920 0.925 0.930 0.935

k2: 100k2: 200k2: 943

10 30 50 70 10 30 50 70 10 30 50 70 10 30 50 70 10 30 50 70 10 30 50 70

k1

(a) nDCG in ML-100K.

gamma: 1 gamma: 3 gamma: 5 gamma: 7 gamma: 9 gamma: 10

0.20 0.24 0.28

0.20 0.24 0.28

0.20 0.24 0.28

k2: 100k2: 200k2: 943

10 30 50 70 10 30 50 70 10 3050 70 10 30 50 70 10 30 50 70 10 30 50 70

k1

(b) nDCG@10 in ML-100K.

Figure 1: Results in ML-100K. Line styles: dashed=SLIM, dash&dot=MF, solid=gSLIM.

Results. Table 1 presents the results, showing that in the ML- 100K dataset SLIM is outperformed by both gSLIM and regular- ized MF, indicating that this smaller and denser dataset (6.3%) might be better served by a traditional MF (hypothesis 1). How- ever, in the larger and sparser ML-1M dataset (4.19%) SLIM out-

1http://grouplens.org/datasets/movielens/

2http://lenskit.org/

3http://www-users.cs.umn.edu/~xning/slim/html/

0.925 0.930 0.935 0.940

0.925 0.930 0.935 0.940

0.925 0.930 0.935 0.940

k2: 100k2: 200k2: 943

1 2 3 4 5 6 7 8 9 10

γ

(a) nDCG in ML-1M

0.18 0.20 0.22 0.24

0.18 0.20 0.22 0.24

0.18 0.20 0.22 0.24

k2: 100k2: 200k2: 943

1 2 3 4 5 6 7 8 9 10

γ

(b) nDCG@10 in ML-1M Figure 2: Results in ML-1M dataset. Line styles: dashed=SLIM, dash&dot=MF, solid=gSLIM.

performs both MF and gSLIM in nDCG@10, but gSLIM outper- forms both in terms of nDCG, indicating that reconstructing the denser latent user matrix with prototypes can support a recommen- dation task beyond a small top-N (hypothesis 2). Finally, figures 1a and 1b show the behavior in the ML-100K dataset of the pa- rametersk1,k2andγin gSLIM performance compared to SLIM and MF baselines, and a similar behavior is seen in ML-1M dataset (Figures 2a and 2b). Analysis shows that largerk2andγgive bet- ter performance butk1has its peak between 20-30 latent factors for users’ matrixU.

4. CONCLUSIONS AND FUTURE WORK

In this work we introduced a general formulation for SLIM,gSLIM.

Although we haven’t addressed the limitation of our model in terms of computational complexity, we think that our results open an op- portunity for studying unexplored ideas on Sparse Linear Methods.

In the first step, researchers can try different methods to learn a denser representation of theA,UorVmatrices. In the step where we perform sparse coding, we could try reconstructing the items’

latent factor matrixVrather thanU, or try directlyA. We can also try alternative algorithms to OMP for prototype learning. Even more, our intuition is that learning these sparse prototypes can help us finding actual “stereotypes” of users and items, which can be used, e.g., for clustering users and items.

5. REFERENCES

[1] Santosh Kabbur, Xia Ning, and George Karypis. Fism: factored item similarity models for top-n recommender systems. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 659–667. ACM, 2013.

[2] George Karypis. Evaluation of item-based top-n recommendation algorithms. InProceedings of the Tenth International Conference on Information and Knowledge Management, CIKM ’01, pages 247–254, New York, NY, USA, 2001. ACM.

[3] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems.Computer, 42(8):30–37, August 2009.

[4] Julien Mairal, Francis Bach, Jean Ponce, and Guillermo Sapiro.

Online dictionary learning for sparse coding. InProceedings of the 26th Annual International Conference on Machine Learning, ICML

’09, pages 689–696, New York, NY, USA, 2009. ACM.

[5] Christopher D Manning, Prabhakar Raghavan, Hinrich Schütze, et al.

Introduction to information retrieval, volume 1. Cambridge university press Cambridge, 2008.

[6] Xia Ning and G. Karypis. Slim: Sparse linear methods for top-n recommender systems. InData Mining (ICDM), 2011 IEEE 11th International Conference on, pages 497–506, Dec 2011.

[7] Xia Ning and George Karypis. Sparse linear methods with side information for top-n recommendations. InProceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12, pages 155–162, New York, NY, USA, 2012. ACM.

[8] Yong Zheng, Bamshad Mobasher, and Robin Burke. Cslim:

Contextual slim recommendation algorithms. InProceedings of the 8th ACM Conference on Recommender Systems, RecSys ’14, pages 301–304, New York, NY, USA, 2014. ACM.

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