# Cold-Start Recommendations for Audio News Stories Using Matrix Factorization

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5 (a) Linear and memory-based models. (b) Non-linear models. Figure 1: RMSE for all models as number of observations per user increases. Item-cold Item-warm User-cold User-warm Size: 23% Size: 25.7% ENS:.4106 ENS:.3518 NLR:.4142 NLLCE:.3566 MFR:.4156 CNLR:.3600 CLNR:.4167 NLR:.3604 NLLCE:.4169 MFR:.3613 Size: 27.1% Size: 24.2% ENS:.4109 ENS:.3482 NLR:.413 NLR:.3525 MFR:.4135 CNLR:.3526 CNLR:.4160 MFR:.3531 NLLCE:.4172 NLLCE:.3533 Table 2: Comparison of models in different item-user and cold-warm quartiles. Users with fewer than 130 ratings are user-cold, and items with fewer than 30 ratings are item-cold. 7. Nearest Neighbor Collaborative Filtering (NNCF): Another way to deal with cold-start is to use the item-item similarity between the feature vectors instead of using the itemitem similarity based on ratings. We use Latent Semantic Analysis (LSA) to reduce the dimension, and after the transformation to the lower dimension, i is the reduced dimension of S i. To estimate the rating R i,u, we can estimate it as the average ratings of all item j for user u, weighted by Pearson s correlation between the reduced dimensions: Sim(i, j) =1+Pearson( i, j ). The resulting hypothesis is: 1 X y i,u = P Sim(i, j)r j,u j2t u Sim(i, j) j2t u We found better results by defining different thresholds to prune from the weighted average ratings with low similarities. We compute the average result over set of fixed thresholds. 4.4 Adding temporal dynamic The user s taste can change over time. For example, users who prefer technology stories might at some point begin to prefer sports stories. In order to address this, we assume an exponential decaying window, and add it in the cost function. For example, for linear regression model, we can update cost Figure 2: RMSE as the number of ratings per item increases. function as following: J(, )= 1 X e ct(i,u) e 2 i,u + ( ) 2 2 (i,u)2t Where c is a small number that control decaying strength, and the t(i, u) is the seconds between training time, and when the user u listens to the story i. We add temporal dynamic to all model-based models (except RLFM). 4.5 Ensemble (ENS) Given that we expect some models to work relatively better under different conditions (e.g., cold/warm items, cold/warm users), in the last approach, all of models are combined to create an ensemble model. To avoid overfitting, we run models with different hyper-parameter values, for example with 5, 10, 15, and 20 latent factors, and let the ensemble model find weights of each model. In this model, we first split the results of all models based on number of user activities in training. For example, we group the output of all models for users that have less than 5 activities together, and between 5 to 10 activities together. After splitting, we can use the ridge regression on each split, where the independent variables are the output of the models, and the dependent variable is the truth value of ratings. In our implementation, we use 26 total model variants to create the ensemble model. 1379

6 5 Evaluation and results We use a dataset from an existing mobile radio application. In our experiments, we use 975K activities from March 2nd to August 3rd, 2015, containing 51K users and 26K news stories. The term vectors are derived from over 1.8M tokens and 123K terms (unigrams and bigrams); we remove terms on a stop list as well as those appearing in fewer than 3 documents. In the evaluation process, to reflect the real-world setting, we train on prior activities to infer the next hour of activity. In practice, we find that using only 7 prior weeks activities is sufficient. For example, for testing on activities on July 1st between 10:00am and 11:00am, we train on the data from May 14th, 10:00am to July 1st, 10:00am. We run 26 model variants for activities from April 20th to June 7th, and save the model predictions. Then we use this output to train the ensemble model on activities from June 8th to August 3rd. Figures 1(a) and Figure 1(b) compare results of different models on the test set. In these figures, the y-axis is RMSE and the x-axis is the the number of user ratings in the training set. As a result of user cold-start, the RMSE is higher when we have few user ratings, and it gradually decreases by increasing number of ratings. Figure 1(a) compares linear and memory-based models. As expected, the baseline has highest error. According to this plot, memory-based models (CCF and NNCF) have slightly higher error than linear models (REG and RLFM) in the user cold-start state, but their error is lower in the user warm-start state. Figure 1(b) compares non-linear models. According to this plot, non-linear models have lower error than both linear models and memory-based models. MFR has higher error than other non-linear models. The difference between the NLR and the CLNR models is not significant, and the clustering does decrease the error of NLR model (most likely because we do not have user demographics), but can generate playlists that are more customized to the specific user. Our proposed NLLCE method has the lowest error. In addition, as expected, the ensemble model significantly decreases the error compare to all other models. Thus, it appears that the ensemble approach is able to combine the best of each model. In the next experiments, we followed prior work to partition the user-item space into four roughly equal quartiles [Park and Chu, 2009]. To create quartiles, we classify users that rate fewer than 130 items as the user-cold set, and remaining users as the user-warm set. Similarly, we classify items that have been rated by fewer than 30 users as the itemcold set, and remaining items as the item-warm set. Table 2 shows results of the top 5 models for each quartile. The ENS model is the top model in all quartiles. According this table, the NLR model, which predicts almost the same playlist for all users, is the best stand-alone model for the user cold-start state. This makes sense, since collaborative filtering has little information to work with for cold users. For the item cold-start state, NLLCE works much better with the user warm-start state. In addition, this table shows that dealing with the user cold-start state is much harder than the item cold-start state (which we again attribute to lack of user attributes). When we have warm-start for both Figure 3: Warm and cold start comparison for the ensemble model over one month (x-axis is days, y-axis is the daily average of ratings). users and items, NLR and NLLCE have similar results. Thus, we find NLLCE to be the best model for users with at least 130 observations, and NLR to be the best model for users with fewer than 130 observations. To investigate item cold-start, Figure 2 plots RMSE as the number of ratings per item increases. We sort all predicted ratings by the number of ratings the target item has at prediction time. We then compute a sliding average RMSE, using a window of size 200. From this figure, we can see that most items have few ratings (the median is 31 ratings). Moreover, NLLCE has lower error than NLR up to items with around 50 ratings. At this point, we suspect that NLLCE begins to overfit in the collaborative filtering component. Finally, Figure 3 compares ensemble prediction in chronological order for the different quartiles for July The x-axis shows the day of the month, the solid line shows the daily average of the truth ratings, and the dash line show the average of the ensemble model prediction for each day. According to this figure, while the ensemble model has some error in the user cold-start states, the error is much lower in the user warm-start states. In addition, the results of the item warm-start state is slightly better than the the item cold-start state, but the difference is small. This shows again that the model can handle the item cold-start challenge. 6 Conclusion and future work In this paper, we propose several models to address the coldstart in recommendation systems. Our results suggest that the Non-Linear Local Collective Embedding (NLLCE) model has lower error than other models in the item cold-start states. 1380

7 In addition, by simplifying the NLLCE model, we create the Non-Linear Regression (NLR) model that has better results in the user cold-start states. Finally, we find that blending different models with the ensemble method can significantly improve results in both the cold-start and the warm-start for users and items. In the future, we will investigate the effect of adding social media and demographic attributes of users, and create new models that deal better with the user cold-start state. References [Adomavicius and Tuzhilin, 2005] Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6): , [Agarwal and Chen, 2009] Deepak Agarwal and Bee-Chung Chen. Regression-based latent factor models. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 09, pages 19 28, New York, NY, USA, ACM. [Bennett and Lanning, 2007] James Bennett and Stan Lanning. The netflix prize. In Proceedings of KDD cup and workshop, volume 2007, page 35, [Breese et al., 1998] John S Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pages Morgan Kaufmann Publishers Inc., [Claypool et al., 1999] Mark Claypool, Anuja Gokhale, Tim Miranda, Pavel Murnikov, Dmitry Netes, and Matthew Sartin. Combining content-based and collaborative filters in an online newspaper, [Das et al., 2007] Abhinandan S. Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. Google news personalization: Scalable online collaborative filtering. In Proceedings of the 16th International Conference on World Wide Web, WWW 07, pages , New York, NY, USA, ACM. [Gantner et al., 2010] Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. Learning attribute-to-feature mappings for cold-start recommendations. In Data Mining (ICDM), 2010 IEEE 10th International Conference on, pages , Dec [Hu et al., 2008] Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In ICDM, pages IEEE, [Johnson, 2014] Christopher C Johnson. Logistic matrix factorization for implicit feedback data. NIPS, 27, [Liu et al., 2010] Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. Personalized news recommendation based on click behavior. In Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI 10, pages 31 40, New York, NY, USA, ACM. [Mcfee et al., 2012] Brian Mcfee, Student Member, Luke Barrington, and Gert Lanckriet. Learning content similarity for music recommendation. IEEE TASLP, [Park and Chu, 2009] Seung-Taek Park and Wei Chu. Pairwise preference regression for cold-start recommendation. In Proceedings of the Third ACM Conference on Recommender Systems, RecSys 09, pages 21 28, New York, NY, USA, ACM. [Rendle and Schmidt-Thieme, 2008] Steffen Rendle and Lars Schmidt-Thieme. Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 08, pages , New York, NY, USA, ACM. [Royston et al., 2006] Patrick Royston, Douglas G Altman, and Willi Sauerbrei. Dichotomizing continuous predictors in multiple regression: a bad idea. Statistics in medicine, 25(1): , [Salakhutdinov and Mnih, 2008] Ruslan Salakhutdinov and Andriy Mnih. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems, volume 20, [Sarwar et al., 2001] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, WWW 01, pages , New York, NY, USA, ACM. [Saveski and Mantrach, 2014] Martin Saveski and Amin Mantrach. Item cold-start recommendations: Learning local collective embeddings. In Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 14, pages 89 96, New York, NY, USA, ACM. [Schein et al., 2002] Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages ACM, [Takacs et al., 2008] G. Takacs, I. Pilaszy, B. Nemeth, and Domonkos Tikk. Investigation of various matrix factorization methods for large recommender systems. In Data Mining Workshops, ICDMW 08. IEEE International Conference on, pages , Dec [Vandenoord et al., 2013] Aaron Vandenoord, Sander Dieleman, and Benjamin Schrauwen. Deep content-based music recommendation. In C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages Curran Associates, Inc., [Zhang et al., 2006] Sheng Zhang, Weihong Wang, James Ford, and Fillia Makedon. Learning from incomplete ratings using non-negative matrix factorization. In IN PROC. OF THE 6TH SIAM CONFERENCE ON DATA MINING (SDM, pages ,

Collaborative Filtering Radek Pelánek 2015 Collaborative Filtering assumption: users with similar taste in past will have similar taste in future requires only matrix of ratings applicable in many domains

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