The Apps You Use Bring The Blogs to Follow

Yue ShiYahoo! Research

Sunnyvale, CA, USAyueshi@acm.org

Erheng Zhong∗

Baidu Big Data LabSunnyvale, CA, USA

zhongerheng@baidu.com

Suju Rajan†

Criteo LabsPalo Alto, CA, USA

s.rajan@criteo.com

Liang DongYahoo! Tumblr

New York, NY, USAldong@tumblr.com

Hao-wei TsengYahoo! Tumblr

New York, NY, USAhaowei@tumblr.com

Beitao LiYahoo! Tumblr

New York, NY, USAbeitao@tumblr.com

ABSTRACTWe tackle the blog recommendation problem in Tumblr formobile users in this paper. Blog recommendation is chal-lenging since most mobile users would suffer from the coldstart when there are only a limited number of blogs fol-lowed by the user. Specifically to address this problem inthe mobile domain, we take into account mobile apps, whichtypically provide rich information from the users. Based onthe assumption that the user interests can be reflected fromtheir app usage patterns, we propose to exploit the app us-age data for improving blog recommendation. Building onthe state-of-the-art recommendation framework, Factoriza-tion Machines (FM), we implement app-based FM that in-tegrates app usage data with the user-blog follow relations.In this approach the blog recommendation is generated notonly based on the blogs that the user followed before, butalso the apps that the user has often used. We demonstratein a series of experiments that app-based FM can outper-form other alternative approaches to a significant extent.Our experimental results also show that exploiting app us-age information is particularly effective for improving blogrecommendation quality for cold start users.

Categories and Subject Descriptors: H.4 [InformationSystems Applications]: Miscellaneous

General Terms: Design, Experimentation

Keywords: Collaborative Filtering, Recommender Sys-tems, Cross-domain Recommendation, Social Networks,Factorization Machines.

1. INTRODUCTION

∗Work done when the author was at Yahoo!.†Work done when the author was at Yahoo!.

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Figure 1: An example of Blog recommendation inTumbr from a mobile device.

Tumblr1 is a major social media site for microblogging.Similar to Twitter2, the social network structure in Tum-blr is directed, i.e., a user can follow a blog without thefollowee’s confirmation, nor being followed back. However,Tumblr is known to be different from Twitter in the sensethat it is much richer in text and images [3]. One key aspectof the growth of Tumblr is to engage the users with morerelevant blogs to follow. Thus, one of the most critical tech-nical challenges in Tumblr becomes how to make blog rec-ommendations that the users are likely to follow. As mobiledevices become ubiquitous, this challenge calls for specialattention, since it has a great impact on the mobile experi-ence of Tumblr users. For example, for a user who mainlyaccesses social media sites from mobile, it would be very

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difficult to make blog recommendations to this user if sheis new to Tumblr. Failing to find interesting blogs quicklywould cause user abandoning the site in an early stage, andthus, being harmful for the growth of Tumblr in mobile. Fig-ure 1 shows an example of the blog recommendation modulein Tumblr from a mobile device. A user can scroll the screento see blogs recommended in the ”Recommended Blogs” sec-tion. If the user is interested in one blog, she can click the”Follow” tap to follow the blog and then she will be notifiedall the updates in the blog. Undoubtedly, having user fol-low blogs is the key metric that drives our evaluation on theperformance of blog recommendation.

Our goal in this paper is to tackle the blog recommenda-tion problem in the mobile domain. We shall emphasize twocritical issues that we face in order to successfully addressthis problem.• Scalability. Tumblr is one of the largest social net-

works in the world. Blog recommendation in Tumblrinvolves hundreds of millions users and tens of billionsinteractions between users and blogs. Therefore, weneed a recommendation system that is able to processthe data at such a scale. In addition, we may exploiteven richer source of information that would make thescale of the data much larger. For this reason, it is ex-pected that our recommendation framework is highlyscalable to very large datasets.• Cold Start. One naive way to make blog recommen-

dation is to adopt the conventional collaborative fil-tering (CF) methods, i.e., to derive recommendationsbased on the user-blog matrix. In other words, we canmake use CF methods to generate blog recommenda-tions for a user based on what the user has followedbefore. The performance of such methods can be verylimited for the users who only have a small numberof followed blogs, so called the cold start problem.Since the long tail is a deterministic property of so-cial networks, we need to make specific contributionsto improve the recommendation performance for thetail users. One straightforward consideration is to ex-ploit the demographic information of the mobile users,in addition to the limited following history. Note that,we suppose, in most cases, the basic demographic in-formation of users is available from mobile devices.However, the basic demographic information may notbe discriminative enough for serving personalized blogrecommendation to individual users. For example, thegender of the user may only be able to distinguish alimited categories of blogs for the user, while not be-ing able to capture a wide scope of the user’s interest.For this reason, it is desired that we exploit extra richsources of information that can help to infer the un-derlying user interests from mobile devices.

In this paper, we propose a blog recommendation systemfor Tumblr mobile that specifically addresses the aforemen-tioned issues. We exploit distributed implementations oflatent factor models, in particular, Factorization Machine(FM) [12], which can scale to the large datasets as in Tum-blr. In addition, the intrinsic property of FM allows us toincorporate rich sources of information beyond the user-bloginteractions. To address the cold start problem, we proposeto use the app usage information of mobile users to infer theuser interest. As the experience of mobile users is dominatedby the apps that they use, it is reasonable to learn user in-

terest from mobile apps. Our assumption is that the appsthat a user often uses may indicate the topic interests of theuser, which can be generalized to the domain of blog rec-ommendation. We collected a large set of app usage data ofmobile users, and demonstrate the correlation between appusage and blog topics. We further conduct a series of ex-periments showing that great improvement can be achievedfor blog recommendation in Tumblr by exploiting app usageinformation.

Our contributions in this work can be summarized intwofold:• We analyze and identify in the mobile domain the rela-

tionship between Tumblr blogs and mobile apps, brin-ing new insights to the application of blog recommen-dation.• We propose an approach by using FM to exploit app

usage data for blog recommendation and show its effec-tiveness through experiments on a large scale dataset.

The paper is organized as follows. In Section 2, we re-view related work, and position our work with respect toit. In Section 3, we analyze the data in the problem do-main and discuss our motivation. After that, in Section 4we present the details of the recommendation approachesfor blog recommendation, especially for exploiting apps inthe FM framework. Then, in Section 5, we demonstratethe results of our experiments, followed by Section 6 thatconcludes the paper.

2. RELATED WORKOur work is related to several aspects in the area of rec-

ommender systems. In this section, we discuss three keyresearch topics that are closest to our work in this paper.

2.1 Social RecommendationBlog recommendation in Tumblr falls into the intersec-

tion between recommender systems and social networks, aparticular area known as social recommendation [6, 22, 20].Great efforts have been devoted to social recommendationin the research community, in which conventional collabo-rative filtering approaches, such as matrix factorization andrandom walks, are extended to incorporate the informationfrom social networks for further improving recommendationperformance [7, 11]. As an industry practice, our work isclose to the work reported from other social network com-panies, such as LinkedIn feed recommendation [1, 2] andfollower recommendation at Twitter [5]. The difference liesin that 1) we target a new problem domain, i.e., blog recom-mendation in Tumblr; and 2) we focus on leveraging externalauxiliary information to address the cold start problem.

2.2 Latent Factor ModelsThe recommendation framework we build on in this work

is Factorization Machines (FM) [12], which is a particu-lar class of latent factor models. Latent factor models haveshown their effectiveness for improving recommendation per-formance through several public competitions in the pastyears, such as in Netflix Prize competition [8] and KDDCUP [4]. The core concept of latent factor models is tolearn low rank representations of users and items so thatthey are indicative for users’ preference to items. FM wasproposed as a generalized latent factor model, one of its keyadvantages being able to incorporate additional informationsources in a uniform fashion [12, 14]. Since its inception,

(a) Distribution of blogs (b) Distribution of blog users

Figure 2: Distribution of blog following data

(a) Distribution of apps (b) Distribution of app users

Figure 3: Distribution of app usage data

FM has been widely recognized and adopted in a number ofuse cases, such as context-aware recommendation [16] andcross-domain recommendation [10]. Our blog recommenda-tion system is built on FM that allows us to exploit auxiliaryinformation sources in addition to the user-blog interactions.However, we implemented FM in a distributed fashion basedon our internal infrastructure so that it can scale to verylarge datasets. Moreover, our focus is application-specificfor blog recommendation, while not on improving the FMmodeling framework.

2.3 Cross-Domain RecommendationOur work is also related to the research topic of cross-

domain recommendation, which generally refers to the rec-ommendation scenarios that involve multiple application do-mains [20]. Prior work has shown that sharing knowledgebetween different product domains can bring up mutual ben-efits. For example, the users may have similar interest inmovies and books [9]. It is also shown that auxiliary infor-mation sources can be exploited as intermediate for linkingdifferent domains, e.g., tags may be common between thebook domain and the movie domain [19], and users may havesimilar rating patterns across different product domains [9,10]. We consider that external information sources are criti-

cal for addressing the cold start problem for blog recommen-dation. In particular, our work is specialized in the mobiledomain, in which we exploit the rich user feedback fromtheir interactions with mobile apps. Our assumption is thatthe apps that a user often uses can indicate her underlyinginterests, which can be leveraged for blog recommendationin Tumblr.

3. APPS AND BLOGSIn this section, we analyze the characteristics of blog fol-

lowing data and app usage data, which motivate our pro-posed solution of exploiting the users’ app usage behaviorto improve blog recommendation in Tumblr. We collecteda sampled dataset of blog following in Tumblr, containinga bipartite graph that shows which user follows which blog.The detail of the dataset is presented in Section 5, wherewe demonstrate our experimental evaluation. The app us-age dataset is collected internally from the user action logsin mobile devices for one week. For the concern of businessconfidentiality, we can not disclose the detail of the processof collecting the app usage data in this paper. Since ourtarget is on blog recommendation, we neglect the users whohave no blog following information. In addition, for eachuser we only take into account up-to 10 apps that she most

Figure 4: Similarity between popular apps and pop-ular blogs.

frequently uses. We leave more detail of the dataset in Sec-tion 5.

Figure 2(a) shows the blog distribution in our dataset.As expected, the distribution follows a power law, whichindicates that a small number of blogs are followed by agreat number of users, while there are massive blogs that arefollowed by very few users. For example, in this dataset weobserve that there are more than 10 million blogs followedby less than 10 users, while there are less than 10 blogsfollowed by more than 1 million users. This characteristicimplies that it would be very challenging to recommend thetail blogs, due to the high data sparseness. In addition, wealso observe a power law distribution for the users, as inFigure 2(b). Most users would only have been following alimited number of blogs, resulting in a natural cold startsituation for recommending personalized blogs to individualusers.

On the other side, we show in Figure 3(a) that the dis-tribution of the apps used by the users is close to a normalshape. It indicates that the majority of apps are sharedacross the average of the user population, which means thatapps might play a better role of carrying over user interestthan blogs. Moreover, in Figure 3(b), we can see that thesituation of cold start users from the app’s point of viewis substantially alleviated, compared to the blog’s point ofview as in Figure 2(b). As such, it is a reasonable strategyto exploit apps that the users use to improve blog recom-mendation in Tumblr.

In order to further understand the relationship betweenthe apps that a user has and the blogs that the user follows,we investigate the correlation between apps and blogs, bymeans of measuring the cosine similarity between the twobased on their users. In other words, if an app is consideredsimilar to a blog, it means that probably a lot of users whouse that app also follow that blog. For the convenience ofvisualization, we focus on the top 20 most popular apps andthe top 20 most popular blogs (popular in the sense of thenumber of users that uses or follows), and show their similar-ities in Figure 4. As can be seen, the apps do share similar-ity with the blogs, indicating that it is reasonable to exploitapps for connecting users to blogs. In addition, we can alsosee that even among the top popular apps and blogs, theydo not have consistent similarity pattern. In other words,some apps may be more similar to one blog, while someother apps may be more similar to another. Therefore, it isalso reasonable to believe that mobile apps are sufficientlydiscriminative for indicating user interests. In the followingsection we will present the technical detail of the recom-mendation approaches, in which we particularly focus onexploiting app usage data.

4. BLOG RECOMMENDATION MODELAs shown above, there are strong correlations between

users’ app usage logs and their blog following patterns. Inthis section, we present in this section two algorithms, onebaseline approach known as Item-based Collaborative Fil-tering (ItemCF) [17], and one proposed approach built onFactorization Machines (FM) [15] which demonstrates howto utilize app usage logs for improvements of blog recom-mendations.

4.1 Problem FormulationLet U denote the user set, V denote the blog set, m be the

number of users and n be the number of blogs. The followinggraph can be defined as a sparse matrix R ∈ Rm∗n, whereRij ∈ 1, ?, Rij = 1 denotes that user ui ∈ U followedblog vj ∈ V and Rij =? means unobserved. In addition,we have side information of users, i.e., their app usage logsA ∈ Rm∗c, where c is the number of apps. Typically, A isa sparse matrix as well since users only use a few of apps.Generally, the task of blog recommendation is to predictwhich users will follow which blogs based on the observedfollowing graph and available side information. Formally, weaim to build models to approximate the observed data byminimizing some loss functions with regularizations.

minF

Ω(R,A,D|F ) + λR(F ) (1)

where λ is the regularization parameter. As follows, we in-troduce two common algorithms to construct the model F .

4.2 Item-based CFAs introduced in [17], ItemCF (also known as KNN) has

been widely used in various recommendation systems. Itsbasic idea is to construct the similarities among differentitems, and then based on users interacted items, other sim-ilar items can be recommended to users. Formally, letui ∈ Rn denote the users’ interactions with items, andS ∈ Rn∗n denote the similarity matrix between items.There are two typical criteria, including cosine measure andPearson-correlation, to construct the similarity matrix S.Let vi ∈ Rm denote the interactions between the item viand users. Then the consine similarity between two items viand vj can be computed as

Sij =vivj

||vi||2 ∗ ||vj ||2(2)

For Pearson-correlation, the similarity is

Sij =

∑k∈1:m |vik − vi||vjk − vj |√∑

k∈1:m(vik − vi)2√∑

k∈1:m(vjk − vj)2(3)

where vj is mean of all elements in vj .After that, the similarities between the user ui and items

can be modeled as

F (ui) = uiS (4)

Finally, the most similar items except those which have beeninteracted by users are kept as recommendations. Typically,to improve accuracy and reduce computational cost, only Kmost similar items are kept for each item, that makes S be asparse matrix where each row contains K non-zero entries.In our work, we build the KNN of blogs based on the user-blog follow relations, and predict what blogs a user wouldfollow based on the blogs that the user has followed.

4.3 Factorization MachinesFactorization machines (FM) have been demonstrated as

a state-of-the-art generic framework for response prediction,such as personalized ranking [15], collaborative filtering [12],click prediction [13], etc. It can mimic different kinds of rec-ommender models by adjusting the input data. Formally,let (x ∈ R`, y) be an instance, where x is the feature vector,y is the corresponding response, f is the number of features.Suppose there are ` instances, and then we have a data set(X,Y ) = xi, yi`i=1. FM aims to build a polynomial func-tion F , such that each feature vector can be mapped to thecorresponding response, by minimizing some loss functions.

minF`(Y,X|F ) (5)

Specifically, d-order polynomial function can be defined as

F (x) = w0+

n∑i=1

wixi+

d∑g=2

n∑i1=1

· · ·n∑

ig=ig−1+1

(Πg

j=1xij

)wijgj=1

(6)where w0 is the zero-order parameter, wi is the first-orderparameter of the i-th feature and wijgj=1

is the correspond-

ing high-order parameters. Different from the traditionalpolynomial regression, FM factorizes the model parametersof high-order features into low-rank representations, where

wijgj=1=

d∑g=1

Πgj=1z

(g)ij ,f

(7)

and v is a latent feature vector with k dimensions. In real-world applications, we typically set d = 2. In this case, wecan rewrite FM as

F (x) = w0 +

n∑i=1

wixi +

n∑i=1

n∑j=i+1

zizTj xixj (8)

As F is convex with respect to w0, wi and vi individually, wecan adopt gradient based methods to learn model parame-ters. From machine learning point of view, this factorizationtechnique reduces the model complexity, where high-orderparameters are represented in compact formats, and hencethe risk of overfitting. From recommendation system pointof view, since the similarity between different features canbe captured by their dot-product scores, FM is also repre-sentative for latent factor models in collaborative filtering.

One typical application of FM is the standard matrix fac-torization (MF) [8]. In MF we typically factorize the matrixR into two low-rank matrices U and V , or

Rpq = b0 + bp + bq + UpVTq (9)

where b0, bp, bq are the global, user and item biases respec-tively and Up and Vq are the corresponding user and itemfactors, respectively. Here what we observe are only usersand items as well as users’ responses on items. Thus, eachinstance in MF can be represented as a triple, which con-tains user and item indices as well as the response. Underthe FM framework, we represent each instance as a sparsevector with m+ n dimensions. There are only two non-zeroentries of which values are 1, corresponding to the user anditem indices respectively. Suppose the user index is p andthe item index is q, and then the prediction of the instanceis represented as

f(x) = w0 + wp + wq + zpzTq (10)

This equation is exactly the same as Eq.(9).

4.4 Modeling App UsagesAs shown above, these two models both rely on users’ in-

teractions on items. If users’ interaction records are limited,we may hard to infer users’ preference and fail to provideaccurate predictions. However, if one user has related auxil-iary information, such as the app usage logs as we shown inSection 3, we may use this information to improve our mod-eling on users’ interests and thus boost the recommendationperformance. We describe how to utilize the two algorithmsto build recommendation models using app usage logs. Themotivation is to embed the correlation between apps andblogs into the model building process.

For KNN, similar with building the similarities betweenblog, we also build the similarity between apps and blogsSa ∈ Rc∗n using Eq.(2) or Eq.(3) and then the combinedprediction for the user ui is computed as

αAiSa + (1− α)uiS (11)

where α is the trade-off parameter which can be learnedfrom a hold-out dataset during the training process. Afterthat, most similar blogs of the user are selected as recom-mendations.

For matrix factorization, the extension is also straight-forward. Besides modeling the interaction between usersand items, we also model the correlations between users’used apps and the followed blogs. Formally, we turn theEq.(9) into

Rpq = b0 + bp + bq +∑

Api 6=0

bi + UpVTq +

∑Api 6=0

WiVTq (12)

where Wi represents the latent factors of the i-th app andbi is the corresponding bias. We can also transform thisequation using the FM formula. For each training instance,besides the user and item indices, we can also append appsused by users and make each training example as a vectorwith length m + n + c. The non-zero values in the vectorrepresent the indices of the corresponding user, blog and allapps used by the user. Then we obtain

f(x) = w0 + wp + wq +∑

Api 6=0

wi + zpzTq +

∑Api 6=0

zizTq (13)

We observe that, the correlations between apps and blogsare built through

∑Api 6=0 ziz

Tq .

5. EXPERIMENTSIn this section, we present a series of experiments that

evaluate the performance of the proposed recommendationapproaches for blog recommendation. We first give a de-tailed description of the datasets including both blog fol-lowing and app usage, and the settings of our experiments.Then, we compare and discuss the performance of the pro-posed approaches against a set of baselines. The purpose ofour experiments is to validate the effectiveness of exploitingapps for improving blog recommendation in Tumblr.

5.1 DatasetsAs mentioned in Section 3, our blog following dataset is a

sampled set of user-blog following bipartite in Tumblr. Thedataset contains over 2 million users and over 20 millionblogs with overall more than 5 billion follow events between

them. The data sparseness is severely at 0.01%, which illus-trates the obvious challenge of blog recommendation. Theapp usage dataset is collected internally for the users whoare involved in the Tumblr dataset. To reduce the noise inthe dataset, we only keep the top 10 apps that the user usedmost frequently in the past week. In total, we have morethan tens of thousands apps in the dataset. For businessconfidentiality, we cannot present more detailed informationof the apps in our collection. Note that as described in Sec-tion 4, we take the apps that each user has as the user sidefeatures in FM. Therefore, in FM we learn not only latentfactors of users and blogs, but also latent factors of apps.

5.2 Experimental SetupIn the blog follow dataset, for each user we split his fol-

lowed blogs into 80% for training and 20% for testing. Inthe test set, for each user we include a set of randomly se-lected blogs, which are in the number of five times as manyas the ground truth blogs. The evaluation is based on howaccurately we recommend the ground truth blogs againstthe randomly selected blogs for individual users. For exam-ple, if one user has one ground truth blog in the test set,we will include another five randomly selected blogs in thetest set for this user. As a result, a random recommendationapproach for this user would result in a probability of 1/6for getting the ground truth blog on top.

To measure the recommendation performance, we adoptstandard metrics, precision at top-N (P@N) and mean recip-rocal rank (MRR) [21]. P@N measures the ratio of relevantblogs we recommend in the top-N list, and MRR is the in-verse of the rank of the first relevant blog, meaning howearly we can make a relevant recommendation. In short,the larger P@N and MRR, the better the recommendationperformance.

Baselines. In our experiments, we compare the perfor-mance of app-based FM to a few baseline approaches aslisted below:• POP. A naive and non-personalized approach that

recommends the globally most popular blogs to everyuser. We generate the most popular blogs based onthe number of follows of each blog in the training set.• ItemCF. This is the conventional item-item CF al-

gorithm [17] that recommends similar blogs to a userbased on the blogs that the user has followed. In ourexperiment we tuned the neighborhood size to be 50,which gives nearly the best performance of this ap-proach.• MF. This is the standard matrix factorization ap-

proach that factorize user-blog follow relations into la-tent factors, which are used further to predict the rel-evance between users and blogs. Note that MF can beseen as a simple version of FM, in which only user-blogrelations are used.

Additional Settings. For both MF and FM we set thenumber of latent factors to be 5, which is in considerationof both accuracy of the prediction and the workload of thecomputation, as it would largely increase both the space andtime complexity if we were to employ larger latent space. Inaddition, for both MF and FM we need to sample negativeexamples in order to train the models. For this reason, ineach case, we randomly selected the same number of blogsas the followed ones for each user to be the negative exam-ples. In this work, we did not further tune the ratio between

Table 1: Performance comparison between app-FMand baseline approaches.

P@1 P@5 MRRPOP 0.225 0.146 0.354ItemCF 0.806 0.614 0.865MF 0.748 0.542 0.839app-FM 0.825 0.628 0.893

positive and negative examples, since the main focus is tovalidate the usefulness of mobile apps for blog recommenda-tion, while not being to tweak the latent factor models.

Implementation. We implemented all the approaches inthe company’s internal Hadoop Map-Reduce cluster withmore than 1000 machines, in order to scale up for the largeamount computation. For POP and ItemCF, the imple-mentation is straightforward, and only requires a few map-reduce jobs. Similar implementation can be found in litera-ture [18]. For the latent factor models, the implementationwith map-reduce is more sophisticated. We refer readerswho have interest to our previous work [23] which elabo-rates the systematic workflows of the implementation.

5.3 PerformanceWe show the overall performance of app-based FM and

all the baselines in Table 1. First, we can see that thepopularity-based blog recommendation is substantially out-performed by all the personalized approaches, indicatingthat personalization is indeed critical for the blog recom-mendation problem. Second, we see that by exploiting appusage information, app-based FM achieves substantial im-provement over ItemCF and MF, both of which only relyon the user-blog follow data. The improvement is over 2.3%in P@1, 2.2% in P@5 and 3.2% in MRR, and all the resultsare statistically significant. This observation indicates thatthe app usage data from the mobile users do carry informa-tion that reflects the user’s interest, and thus, can benefitfor blog recommendation in mobile devices.

We further investigate the impact of the followed blogs peruser on the performance of each recommendation approach.In this respect, we split the users in the test set based onthe number of blogs that the user follows, and measure theperformance for each user group. Specifically, we use “G10”to denote the user group in which each user has less than 10followed blogs in the training set. We show the comparisonresults for P@1, P@5 and MRR, respectively, in Figure 5,in which we come into three observations. First, the POPapproach, as known to be non-personalized, is not stronglyinfluenced by the number of user followed blogs. i.e., P@1remains similar for different user groups, while P@5 andMRR are not consistent in showing the performance pat-tern. Second, in contrast to POP, all the personalized ap-proaches (both baselines and app-based FM) show clear im-pact from the number of user followed blogs. It is consistentthat the performance of all these approaches decreases asthe users follow less blogs, which is intuitive to our under-standing that the recommendation performance would bedegraded for cold start cases. Third, we also notice that al-though the performance degrades when the number of userfollowed blogs decreases, app-based FM achieves relativelyhigher improvement for the users with less followed blogs.

(a) P@1

(b) P@5

(c) MRR

Figure 5: Performance on users with different num-ber of followed blogs.

For example, the improvement of app-FM over ItemCF onthe user group G100 is around 2.9%, while it raises up to16.6% for the user group G5. This result again indicatesthat app-based FM is particularly effective for improvingthe cold start cases in blog recommendation.

6. CONCLUSIONWe address the blog recommendation in mobile domain

by leveraging app usage data into the Factorization Machineframework. Through our experiments on the Tumblr blogfollowing dataset, we show that exploiting the app usagedata in FM leads to a superior performance of blog recom-mendation compared to other baselines, especially for thecold start users who have very limited number of followedblogs. Our future work involves a few directions. First,we would like to deploy app-based FM approach into onlinetest, and compare it with production baselines. Second, weare interesting in exploiting different types of user actions onapps to derive more discriminative user interests, which canbe further used for improving blog recommendation. Third,as this work is the beginning of our contributions to improv-ing recommendation application in mobile devices, we alsolike to explore novel application domains and novel featuressuch as context and network structures.

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