Social-PPM: Personal Experience Sharing and Recommendation

Anne H. H. NguDepartment of Computer Science

Texas State UniversitySan Marcos, Texas, USAEmail: angu@txstate.edu

Shawn FangDepartment of Computer Science

University of Texas, AustinAustin, Texas, USA

Email:qfun@utexas.edu

Helen PaikSchool of Computer Science and Engineering

University of New South WalesSydney, NSW, Australia

Email: hapik@cse.unsw.edu.au

Abstract—The rise in popularity of various social networkapplications has brought the opportunities for Internet usersto share and reuse a plethora of things like images, videos,datasets, ideas, interests, reviews etc. However, currently thereis no effective way of sharing personal experiences such asthe process of filing a personal income tax return or applyingfor a visa. We propose a social-aware process model and itsimplementation as a social network application that empowersusers to create, to execute, and to share personal experienceswithin a social network at anytime and anywhere. As a social-aware process management system, it is important to havean effective recommender that predicts personal processesthat a specific user may not be aware of and yet have theopportunity to enhance his/her life experiences. We adaptan existing collaborative filtering algorithm with emphasison social context, the interaction history, and the type ofinteractions in processes for effective process recommendation.The assumption is that if two users have been copying andfollowing same processes then it is likely that those two usershave similar life goals, and this would be reflected in theirfuture usage of the system as both of them will engage insimilar processes.

Keywords-Personal Process Management; Process Crowd-sourcing; Process Recommendation; Socializing processes

I. INTRODUCTION

The rise in popularity of various social network applica-tions has brought the opportunities for Internet users to shareand reuse a plethora of things like images, videos, datasets,interests, ideas, traffic information, reviews etc. There aremany different types of social network applications designedto encourage the exchange of ideas and the establishment ofa community. However, none of them encourage the trackingand exchange of personal processes. While the rise in thesharing of video clips in Youtube, Snapchat, Facebook andInstagram do compromise personal experiences, those videoclips do not document the insight and the context behind theshared personal processes and cannot be reused effectively.

In the increasingly connected digital world, users carryout personal processes on a daily basis using a variety ofapplications or resources on the web and mobile phones.However, the knowledge gained by users while carryingout the repetitive routines is locked away (in a person’s

Shawn Fang is an REU participant in the Texas State REUIOT program

email folder, personal notebooks, etc.) and is inaccessible toothers. Even though we have 21st century communicationinfrastructure, we have no easy way to reuse or share ourknowledge about how we do what we do. For example, whatdo you do normally to apply for admission to a PhD programat Texas State University in USA? Existing mechanisms ofsharing personal experiences lack a process model that candocument personal experiences in a structured and collectivemanner. So while personal processes may be available, albeitnot in an obvious manner, via existing applications, noneappear to be reusable in a collective learning environment.

We define a personal processes as a list of tasks that auser must perform in a certain order to achieve a goal. Itcan be as simple as obtaining an Internet domain name fora small business or as complicated as applying for a drivinglicense in the state of Texas. Unlike business processeswhich are structured and schema-based, personal processesare ad-hoc to the point where each process may have itsown unique structure. Personal processes are thus muchmore flexible than business processes in the order of howtasks are scheduled and executed. For example users canadd or remove tasks in personal processes as the situationdemands. After the completion of a task, users can write areflection or comment on how the task went, attach pictures,video or documents relevant to the tasks and share that withpeople in his/her social network. This is different from abusiness process which is only interested in recording whenor whether a task has been executed successfully.

We believe that the lack of a systematic approach forusers to create, share, and reuse personal experiences in acollective learning environment is hindering our day to dayproductivity and unnecessarily preventing the accumulationof “crowd wisdom” that can benefit a virtual community. Arecent experiment [1] also strengthens our hypothesis thatthe ability to share personal processes in a structured mannersaves time for subsequent users, as it means that users are notrequired to conduct their own time consuming research oneach process to derive the associated tasks and their contexts.In this paper, we propose a social-aware process model andrecommendation that can document personal experiencesin a structured way which facilities reuse and collectivelearning.

Our goal is to develop an innovative platform for personalprocess documentation and sharing that is accessible viaany mobile devices. Through automatic documentation andsharing of personal experiences, we can avoid costly orinefficient steps in executing routine personal processes. Ourvision for managing personal process requires the develop-ment of the following components:• A mobile client that supports the highly exploratory

process creation and execution (e.g. tasks can be added,deleted, re-ordered, changed and executed with selectedresources)

• An intuitive way to query and search personal processthat allows users to maximize their productivity (e.g.tasks that must be done in the same location, tasks thatneed to be completed at certain time-frame, tasks thatcan done with minimal cost at certain time)

• Sharing of intermediate process execution results withothers and receive feedback from them while carryingout the process.

• A personal process recommendation that leveragescontextual knowledge and social relationships betweenusers

• A 21st century mechanism for passing accumulatedpersonal experiences to others without explicit manualdocumentation.

Our earlier paper [2] describes the general architecture ofour social aware personal process management system thatsupports creation and execution of personal processes. In thispaper, we discuss the need for a structured personal processmodel and the robust process recommendation system thatencourages reuse of processes from users with similar ”be-havior” (i.e, followed or copied similar processes). This isknown as collaborative recommendation.

The remainder of this paper is organized as follows.Section II describes the related work, Section III providesthe overview of the system, Section IV outlines the personalprocess model, SectionV describes the application, SectionVI discuss the recommendation algorithm, Section VII out-lines the experimental results. Finally, Section VIII providessome concluding remarks and also discusses various openissues.

II. RELATED WORK

Relevant work in personal process management can bedivided into three types. The first type is the existing mobileapplications available from Google Play and AppStore. Thesecond type is the various online forums such as wikiHowand eHow and the third type is the published literatures inthis area.

We downloaded and tested highly rated free mobile appson the market that allowed users to organize tasks. Mobileapps that we tested include: MyLifeOrganized1, Wunderlist2,

1www.mylifeorganized.net2www.wunderlist.com

GTasks3, Mindjet Tasks4 and Todoist5. All of those apps,which usually identified themselves as to-do list managers,are simply fancy text editors where the user can write downtasks she needs to do. In the best case, few of those appsoffered a reminder functionality and allowed the user tokeep track of her progress in her to-do list. Mindjet Tasksextended the functionalities to also manage to-do list fora team of collaborators. None of those apps offered thepossibility of reusing and sharing the process/experience ofcompleting the to-do list. In other words, each user is at herown and there is no way of transferring knowledge aboutcommon processes from one user to another without tediousdocumentation.

wikiHow [3] is a web service that sources the crowd tocontribute advices on how to do certain things in the samespirit of wikipedia. The topics addressed in wikiHow canrange from how to drink more water everyday to how tobecome more social. Each topic is a textual description onhow to accomplish a certain thing written by a volunteer.It does not describe any specific personal experiences inachieving a goal. In particular, the contextual informationassociated with processes, such as personal reflection orcomments on whether a task has a time, location, culturalcontext, organization, or specific resource dependency is notdescribed. It is not possible for other users interested in thesimilar process to glean from the wikiHow description onwhy a particular way of achieving a goal is better. eHowuses a blogging paradigm rather than a mass collaborationapproach to achieve similar goals.

The earliest academic research work on personal processmanagement is described in [4]. The authors recognizedthe need to have a personal process model which is notconstrained by rigid control and structural rules as definedin business process management. They proposed a set ofalgebraic operators that can be used to query a repository ofprocesses for recommendation of suitable processes targetedto a task at hand or a specific input/output artifact. However,the users is restricted to process templates pre-created by thesystem. Moreover, there is no notion of sharing, recommen-dation, reuse, following, and notification.

A series of work produced by the authors in [5], [6],[7] defined and implemented the personal process manage-ment concepts from Web service composition view pointswhere the focus is on formally and concretely representingsequential and conditional constraints in a personal processso that it is ultimately executable. In particular, [7] presenteda language named PPML (Personal Process Modelling Lan-guage), and used it to formally describe personal processeswith the required input, and the expected output. Again,personal processes in PPML-based system must be gen-

3GTasks: To-Do List & Task List, Google Play4www.mindjet.com/products/mobile5www.todoist.com

erated from templates created by expert process designersbefore others could use it. It is thus not a tool that canempower end-users to create and share variety of personalprocesses in a large scale. The similar work described in [6]aims to solve the end-user process creation, execution andsharing, but the applications remain restricted to a domainexpert, not designed for everyday processes. [8] described asimplified Business Process Management (BPM) model thatpaid special attention to the role of social aspects of theprocess management such as sharing and assigning tasks.However, this work remain at preliminary level and it didnot address the importance of recommendation to learn aboutnew processes that could be valuable for achieving goals thatusers are not aware of. [9] proposes a graph-based personalprocess model which aims to provide a generic descriptionlanguage for personal ‘how-to’ instructions. The work doesnot yet support the integration of the model to the executionor sharing environment.

There have also been some attempts in recent yearsto accommodate social features in the Business Pro-cesses Management (BPM) environment. Most notably arecommendation-based process modelling support systemwith social features in [10]; a modelling and executiontool for business processes with collaboration and wiki-likefeatures embedded in [11]; and an ad hoc workflow systemfocusing on non-intrusive capturing of human interactionsin [12]. However, these systems aim at recommendationof task elements of a process during the construction ormodeling of a process not prediction of concrete processesthat might enhance a user in accomplishing goals thathe/she is not aware of. The HelpMeOut [13] is a socialrecommendation system that aids novices with the debug-ging of compiler error messages and runtime exceptions bysuggesting successful solutions to similar errors that otherprogrammers have encountered. The main hypothesis behindHelpMeOut is the belief that presenting relevant solutionexamples will make it easier for novices to interpret andcorrect error messages. The relevant solution examples bringthe contextual information how others are approaching thesame problem. This is similar to our goal of recommendingand sharing of concrete processes rather than just abstractdescription as presented by systems like wikiHow and eHow.

The work by Matejka et. al. in [14] describe a recom-mender system that suggests AutoDesk commands to users.They demonstrated that item-based collaborative filteringwith domain specific rules is effective in recommendingnew commands to users. Processes are not characterizedby a fixed set of features like a movie or a book. Theperceived value of a process depends a lot on the contextunder which it is being used and the history of interactionson the process. We adapt a collaborative filtering algorithmwith special emphasis on social context, the interactionhistory and the type of interactions in personal processesfor effective personal process recommendation.

Figure 1. Overview of the System

III. OVERVIEW OF SOCIAL-PPM

Social-PPM is a social network application that runs ona mobile phone with functionalities to (i) document andcreate personal process using a to-do list model, (ii) queryand search personal processes that allows users to reusethe most relevant crowd sourced processes, (iii) leveragecontextual knowledge and social relationships between usersto recommend other personal processes that might improveuser’s productivity, (iv) encourage users to share the inter-mediate results of personal processes with others and receiveimpromptu feedback that can be used to adjust the currentpersonal processes in real-time.

Figure 1 provides an overall architecture view of thesystem depicting the flow of data between the end-user,the server process model, and various other services thatcan be leveraged to complete a process. Upon successfulregistration in our system, users are given a workspace wherethey can use the tools provided by the system to create ormanage their personal processes. For example, when a useris at a specific place and time, the system can alert users ofwhat tasks can be accomplished at that time and place. Ifusers need to create new processes, they can first perform asearch and see whether there are any relevant processes thatcan be sourced from the community to be used as a templateor guidance. If a relevant process is found, creating a newprocess is just a matter of doing a copy and modifying thecopied process for the current situation.

IV. THE PERSONAL PROCESS MODEL

In this section, we describe the model we use to representthe processes in Social-PPM. The model shares somecommonalities with a related system called ProcessBook[15]. Both systems consider a personal process to bedescribed as a simple task list template (i.e., tasks to bedone). The word ‘template’ is used in the sense that it isintended to be customized. A TaskListTemplate is a pair(G, T), where:

• G is a statement of the goal in natural language,• T is a rooted tree where the root is labelled with

a unique identifier for the template. Each descendant

Root{Template ID =1001,

Start}

t1{A, Organise the

graduation gown}

t1.2{B, Collect the gown}

t2{C, Register for the

ceremony}

t1.1{A, Book graduation

gown online}

t4{C, Attend the briefing

session}

t1.3{B, Obtain receipt}

Start* Organise the graduation gown - Book graduation gown online - Collect the gown - Obtain receipt* Register for the ceremony* Attend the briefing session

To-Do List

TaskList Template

Figure 2. TaslListTemplate and To-Do List

node t, which represents an atomic task in the tem-plate, is a triple (id, label, properties), where id

is the unique identifier of the task, label is a textualdescription of the task and property describes othercharacteristic of the task such as the person who firstcreated it, the number of like and dislikes on the taskas well as the set of available comments on the task.

This simple model allows flexible and agile representa-tion of personal processes, which is necessary to supportunstructured or ad-hoc processes. [16] utilizes similar butmore restricted form of to-do list model based in a busi-ness operation management context to support knowledgeintensive processes for the knowledge workers [17]. Theidea of supporting flexibility in a process creates a lessclear boundary between a process model and its runninginstances, as the model can be changed by the users duringthe execution (e.g., users can add a task, or ignore/delete atask). To capture and utilize these kinds of user activities inSocial-PPM, we assume the following interactions betweenmodels and their instances.

In the beginning, a template is chosen (either by recom-mendation used in Section VI or by search), then a to-do listis created (see Figure 2) for the user. From here, the systemtries to capture the activities of the user by modeling thefollowing change operations on each task. There are twotypes of change operations: task planning operations, taskexecution operations.

First, for each task, in terms of its execution life-cycle,it goes through the following states (supported by the taskexecution operations).• Planning Once a task is created in the To-Do list, by

default, it will be placed in the planning state.• In-Progress Once a task is selected and flagged (e.g.,

by tapping a command ) that the task has been started,it is timestamped by the corresponding operation. Onea task is in this state, it can be commented and followedby other users in the network.

• Completed A task moves to “Completed” state whena user indicates (e.g., by tapping the command again)the task has been done. The operation also creates atimestamp. When a task is in this state, the owner ofthe task can write a reflection or comments regardingthe task.

These distinct states of a task allow for the system to trackwhich task in the to-do list has been completed and in whatorder (indicated by the timestamps).

Second, the to-do list can also be changed by the user viathe following set of explicit change operations. We refer tothis as task planning operations as they allow the users toupdate the to-do list. For simplicity, we omit the operationparameters.• Insert() a new task t can be inserted at a given position• Delete() task t is deleted at a given position• Update() the textual description of task t is updatedOf course, the combination of the insert and delete oper-

ations allows for the implementations of high-level changeoperations such as Move() or Replace(). These changeoperations are also logged with timestamps and the locationthe where the changes occur so that the system can learnhow a particular task list template is customized by the userat run-time.

V. THE APPLICATION

In this section we will briefly explain the key features ofthe application by describing different use cases in whichthe user will encounter while using the application. For thispurpose, we will be using a hypothetical personal processof a user applying to a PhD program in the United Stateas an example and show what the user will go throughwhen using this mobile application to create and manage thispersonal process. When the user logs in to the application,the system will present the list of processes that the userowns, a list of processes that the user is following and alist of recommended processes. The interface also presentsthree possible options which are: Manage: managing anexisting process, Delete: deleting a current process, andSearch: searching for a new process as shown in Figure 3.

Creating a New Personal Process: In order to create an“Apply for a PhD” process, the user starts by searching for asimilar process in the system that may serve her purpose. Shecan copy the process if she finds the needed process in thesystem by clicking on the copy button and then assigning aname and description to it. If the user is not satisfied by thesearch results then she can design a process from scratchby clicking on the create a new goal in the searchpage. A new view will appear, allowing the user to supply aname, a description, and a category for the process as shownin Figure 3. Once the information is entered, the user canclick on the create button, and the process will appear inthe user’s current process list.

Managing a Process: Managing a process is availablefrom the application homepage as shown in Figure 3. Theuser can select the target process from the current processeslist and then click on the manage button. The system willreact by opening a new view (Figure 4), where the steps ofthe process will be displayed as a to-do list and the user canmanipulate them using several buttons including:• up and down buttons: allows the user to change the

position of a task in the list. This re-ordering might benecessary due to some personal constraints.

Figure 3. Creating a New Process

• Delete button: deletes a task from the process. Sometasks in a process might not be relevant due to user’sspecific context. For example, if the applicant graduatedfrom a university in United State, there is no need totake the TOFEL test.

• Manual button: allows the manual execution of a task,when pressed, it will change the state of the task tostart. When pressed the second time, it will move thestate of the task to ’complete’ . This function helps toautomatically document information such as the timeand location of where a task is completed. User canalso post a comment on how the task is completed andhow much effort it takes to complete it by going to thedetails button.

• Auto button: allows the user to select a resource eitherlocally on the mobile phone or remotely on the cloudin completing the task, and record that information.Currently, the only available local services are sendingan email and adding an event to the user’s calendar.This function helps to document how a task is beingaccomplished by software.

• Details button: allows the user to make changes inthe task’s name, description, and priority along withother functionalities such as view the location wherethe task was executed, add review/comments relativeto the task, or comment on other people’s reviews if itis a followed process.

• Add New Task button: allows the user to create anew task from scratch and add it to the current process.

• Follow button: allows the user to follow the displayedprocess.

• Search Task button: allows the user to obtained alist of recommended tasks in the repository and add thetop ranked task to the current process’s list of existingtasks.

• Copy button: allows the user to copy the currentprocess structure for reuse.

Figure 4. Managing a Process

VI. THE SOCIAL AWARE PROCESS RECOMMENDATION

In a social aware personal process system with unlim-ited number of users and processes available, effectivelyand efficiently searching and selecting the most relevantprocesses is a crucial challenge. It is possible for usersto perform a keyword based search to find processes thathe/she can reuse. However, the current model of keywordsearch over the textual descriptions of processes is limitedand will returns a set of processes that contains the keywordsregardless of the semantics of the process. In the BPM area,many consider BPM models are graphs and apply a graphsearch algorithm or graph mining techniques to find ‘similar’processes in the relevant process repository. Unlike BPMmodels which have terse task descriptions and highly varyingstructures, personal process models could contain longerand more descriptive task descriptions and relatively simplestructures. So, comparing (graph) structures only may lead toa large number of ‘structurally similar’ personal processes,but semantically different processes.

Given the limited display of a mobile device, returning alarge number of processes will result in information overloadfor the users. A facet search model [18] for processes canimprove the situation but at the expense of taking a muchlonger time to explore the search space to find the relevantprocesses. Another solution is to recommend relevant pro-cesses to users based on their search goals. In the contextof any social based application, the interactions initiated byusers are not completely random and carry rich contextualinformation. We aim to provide a process recommendationservice that can predict processes that the user may wishto pursue to improve his/her productivity based on socialcontext. The algorithm for determining what processes mightbe interesting for a certain user relies on his/her interactionhistory with existing processes in the system, the types ofinteractions, and the user profile. For example if two usershave been copying, commenting, and following the same

processes then there is a high probability that those two usershave similar interests. This would be reflected in the systemas both of them will engage in similar personal processes. Ifthis is the case, our system will pro-actively spot processesthat only the first user is using and suggest them to thesecond user or vice versa.

Our recommendation system is based on collaborativefiltering to recommend a list of processes to a user. Thefinal list of processes is based on a comprehensive scorethat sums up the score of voting and the score of rating foreach neighbor process in a user’s network. An action (copy,create, or follow) made by a user to a process representsa voting. For example, when a user copies a process, onefull vote is assigned to that action. However, when a userfollows a particular process, only half a vote is assigned tothat action. We assume that the action of copying a processhas more weight than the action of following a process. Thisis because the action of copying a process made by a usermeans the user is interested in the copied process to theextend that he/she wants to carry out a similar process byfirst owning a copy of that process. The overall rating scorefor a process is calculated based on the average of ratingsover all tasks in a process. We will first define some of theterminologies used before we present the recommendationalgorithm.

Definition of terms

• Target User is the user whom the recommendationsystem is targeting

• Target Process is a process that the target usercopied/owned/followed

• Target Category is a category that a target process be-longs to (e.g. we can have categories such as education,finance, travel etc)

• Neighbor User is a user who shares at least onecommon process with the target user

• Neighbor Process is a process that a neighbor usercopied/owned/followed

• Neighbor Category is a category that a neighbor processbelongs to

The recommendation algorithm can be divided into twoparts, i.e. determination of the set of processes to be recom-mended, as well as the scoring and ranking of each processin the set.

Part 1: Determination of recommended processesThe purpose is to find the complete set of processes thatthe Target User Ui is predicted to copy or follow in thefuture. This prediction is based on the similarity of usersin their actions. The assumption is: if two users have beencopying and following the same processes then there is ahigh probability that those two users have similar interests,which would be reflected as both of them will engagein similar personal processes in the future. The prediction

procedure is divided into three steps.• Step 1: Find the set of processes the target user Ui

copied, followed or owned, which we call {Pα}i, i.e.the set of processes associated with user i. This isknown as Target Process set.

• Step 2: Find the set of users who owned, copied orfollowed any process in the Target Process set {Pα}iexcluding Ui itself, i.e. {Uj}i = {Uj |Uj owned,followed, or copied any process in{Pα}i and {Uj 6= Ui}}. This is called the NeighborUser set.

• Step 3: Find the set of processes which areowned, copied or followed by any user in Neigh-bor User set {Uj}i, excluding those that are owned,copied or have already been followed by Ui.This set is represented by {Pβ}{Uj}i {Pβ}{Uj}i:= {Pβ |Pβ is owned, copied, or followedby at least one user in {Uj}i} − {Pα}i.The processes to be recommended to target user Ui allcome from the set {Pβ}{Uj}i . We call this NeighborProcess set.

Part 2: Scoring and rankingThe records in the Neighbor Process set {Pβ}{Uj}ineed tobe ranked and presented to a target user Ui according to theorder of scores which is used as the criteria for ranking. Ourscoring system contains two basic schemes, i.e. voting scoreand rating score, which are combined as an comprehensivescore eventually.• Voting score: SC . This score is computed in the follow-

ing steps. For each process Pβ in the Neighbor Processset {Pβ}{Uj}i , for each user Uk in the Neighbor Userset, {Uj}, compute the Jaccard similarity J(Uk, Ui)between the users Uk and the target user Ui, whichis performed as follows: a) Find the set of processesUk owned, copied, or followed and the vote for eachkind of action on each process i.e. {Pα′}k; and {Pα′}v;b) Compute the J(Uk, Ui) = |{Pα′}k ∩ {Pα}i| /|{Pα′}k ∪ {Pα}i | , in which the ”| | ” operator meanscardinality of. iii) SC =

∑Uk∈{Uk}{Pβ}{Uj}i

J(Uk, Ui)

weighted by the number of votes.• Rating score: SR. This score is based on users’ ratings

on processes. Currently, in our system, there are nodirect ratings for a process. However, each task in aprocess have attached reviews and rating. Therefore,the rating of a process is computed as the mean ofavailable ratings of all tasks in this process.

• Comprehensive score: SS . Both SC and SR are non-negative. Therefore, we can normalize the range of eachscore to [0, 10]. Hence, SC and SR becomes SnormC andSnormR , respectively. The comprehensive score SS =SnormC + SnormR

.

Figure 5. Data and Relationships

.

VII. EVALUATION

A thorough user study of our application is our immediatefuture work. Here, we conducted a simple experiment totest the validity of our recommendation algorithm. Weestablished five user profiles, twenty different processes ofvarying categories and rating of tasks, and the relationshipsbetween various users and processes. This is depicted inFigure 5. Based on the relationships between users andprocesses, the recommendation system computes a compre-hensive score for each neighbor process of a target user andrecommends the processes in the correct order to the targetuser as we anticipated. For example, a target user, personA created process 1 and 3, copied processes 2, 4, 5 and7, and followed process 6. This implied that user A hasa relationship to each of these processes and they shouldnot be recommended to user A in our recommendationsystem. Since a process stores information about who hasa connection to it, from the known processes 1-7 of userA, we find out that user B, C, D and E are the neighborusers with user A. Similar to user A, these other users havetheir own relationships to the processes. By finding the setof neighbor users of user A, the other unknown processes touser A but known to the other users will be filtered in. Also,based on the ranking algorithm described in Section VI, theprocesses with ranks greater than certain threshold will berecommended to user A. In this example, ranking of theprocesses from high to low is process 14, 13, 11, 12, 15,16, 8, 9, 10, 17,18, 19 and 20. These are the predicted rankedprocesses in our recommendation system for user A and theactual recommended processes for user A are exactly thesame as the predicted recommended processes.

VIII. CONCLUSION AND OPEN ISSUES

We have proposed a flexible personal process model andits implementation as a mobile social network applicationthat empowers users to create and to share personal pro-cesses at anytime and anywhere. We leveraged the mostrecent functionalities of social networks, including: a fol-lowing system allowing users to keep track of the latestprogresses and comments of personal processes created

by other users, a notification system allowing users to beinformed on major changes of followed processes and a rec-ommendation system that predicts processes that a specificuser may be interested in. In particular, the recommendationsystem enables users to learn about new processes that canbe used to achieve goals that are oblivious to them. Oncea user A shares some common processes with other usersin the social context, all processes related to other users butexcluding the known processes of the user A can potentiallybe recommended. However, the current recommendationsystem in PPM assumes the equal weight of voting (the typeof interactions) and rating (the reviews) in the computationof comprehensive score. Further experiment needs to beperformed to arrive at an accurate weight for voting andrating. The current recommendation algorithm also suffersfrom the “cold start” problem. For example, a new processwhich only has relationship to its creator will never berecommended to other users unless users search for it and theprocess appears in the search result. A content-based filteringleveraging the category that the process belongs to couldsolve this problem. Since every process must belong to aparticular category, by computing content similarity betweenthe newly created process and the existing processes in thesame category, it is possible to derive initially the most likelyNeighbor User set and this new process can be recommendedto users in the neighborhood. We plan to incorporate thiscontent-based filtering into our recommendation algorithmin the future. A comprehensive field study that includes theusability of the mobile social network application and theaccuracy of the recommendation algorithm need to be furtherinvestigated.

Acknowledgment

We thank the National Science Foundation for fundingthe research under the Research Experiences for Undergrad-uates Program (CNS-1358939) at Texas State University toperform this piece of work and the infrastructure providedby a NSF-CRI 1305302 award.

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