Ontology–based Context–dependent Personalization Technology

V.Gorodetsky, V.Samoylov, S.Serebryakov Practical Reasoning, Inc. under the auspices of

St. Petersburg Institute for Informatics and Automation {gor, samovl, sergey_s}@ practical reasoning.com

Abstract

Personalization, a topmost concern of modern rec-ommendation systems (RS), is intended to predict indi-vidual motivation of a customer for this or that choice. It depends on many factors forming explicit and im-plicit decision context. The paper proposes RS person-alization technology that focuses on ontology–based extraction of semantically interpretable context of each particular customer’s decisions from his/her historical data sample with the subsequent machine learning–based extraction of customer–centered feature set and personal cause–consequence decision rules. The tech-nology is fully implemented by Practical Reasoning, Inc. and validated via several case studies.

1. Introduction

During the last decade, recommendation systems (RS) are becoming a subject of ever increasing interest of research and industry. If recently such an interest has been shown mainly by leaders of e-trade market like Amazon.com [8], NetFliX [9], etc., currently it is also shown by small and medium size companies con-sidering RS as effective means of providing high com-petitiveness in modern markets. Now application area of RS is noticeably broadening, getting in travel and entertainment businesses, financial business, etc.

By now, decades of academic and industrial RSs have been developed. Amadesa Customer Experience Suite™ [2] proposes medium–size business solution, which, besides recommendations, is capable to account profitability (using various business rules) and create similar customer segments using multi–attribute model. Aggregate Knowledge Discovery Platform –Dynamic Creative [1] is a web server providing solutions for large online supermarkets and catalogs. It contains rich set of reusable components to access various data formats and supports monitoring of cus-tomer’s activity like clicks, mouse positioning, etc. SUGGEST: Recommendation engine [12] is a free

stand alone software implementing collaborative filter-ing (CF) version surpassing routine algorithm quality up to 30%. It is capable of efficiently processing huge data volumes. Xiam Technologies [15] offers advertis-ing applications targeted to users of mobile devices. Its platform (My Personal Offers System) is targeted to content market and services provision and capable to convey dedicated information/advertisements to the customers via mobile phone channels (WAP, Web messages, etc.). enRICH Personalization engine [3] uses Ensemble Learning producing 40 competing rec-ommendations and capable to process about 200 mil-lions of queries on daily basis. Sematext Recommenda-tion Engine [11] is a rule–based tool realizing simple CF algorithm connected to Web.

Although RS theory and practice has reached some maturity level, its fundamental drawback is limitedpersonalization. The existing RS technologies do not use all available multi–source customer–associated in-formation, do not reveal implicit “driving forces” mo-tivating customer’s preferences and choice. At the same time, available knowledge implicitly contained in multiple data sources possesses rich implicit contextfor each particular act of customer’s choice and it is not practically used. To detect this knowledge and use it for customer profile personalization is a challenge.

This challenge is in the focus of the technology proposed in the paper. The idea behind it is to ade-quately express this context in terms of carefully se-lected personalized feature set of customer’s motiva-tion. In the rest of the paper, section 2 outlines devel-oped technology. Section 3 describes the case study, intelligent e-mail assistant, and demonstrates basic phases of the technology with the focus on how ontol-ogy–based contextual feature set is formed. It also out-lines a technology used to extract context and feature set from text represented in a natural language through text analysis and mining. Section 4 outlines experi-mental results. Section 5 summarizes the paper contri-bution and outlines further research.

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

978-0-7695-4191-4/10 $26.00 © 2010 IEEE

DOI 10.1109/WI-IAT.2010.254

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2. Proposed technology

Personalized RS technology has to cope with many challenging questions, e.g.

How to extract useful information from multiple sources of poorly–structured heterogeneous data (texts, categorical, ordered and numerical) and how to appropriately fuse it for joint use? How to extract and account the context and semanticsof each particular instance of customer's decision act? How to extract useful features/dependencies from da-ta of huge dimensionality to specify well decision act context and to account multi–criteria motivation?

Let us sketch the developed technology (Fig.1). 1. Phase 1: Semantic enrichment of historical data

sample of customer’s activity via building domain on-tology on top of it thus transforming the latter into ob-ject data base (DB). This ontology is customer–independent. It is the scheme of an object DB semanti-cally structuring initially unstructured data sample rep-resenting it as a set of instances of objects. These ob-jects are associated with context and semantics of the customer’s history of decision acts.

2. Phase 2: Expert–based generation of preliminary feature set of unlimited cardinality while requiring that

every feature is either an ontology concept, or a con-cept attribute. This provides for features with clear se-mantics. At this phase, experts can also introduce sec-ondary features defined, e.g., as structures given over the features introduced. For each secondary feature, a concept/attribute should be introduced it the ontology. New concepts and/or attributes (secondary features) should be instantiated too that is done using a query language developed for object DB [7].

When preliminary feature set has been formed cus-tomer’s historical data are transformed to the form of “star”–structured tables where fact table columns cor-respond to elements of the feature set with one row, in kernel table, per every customer decision act. This pro-cedure results in context–dependent representation of historical data in terms of features. Let us note that dif-ferent objects can be of different formats since some features can be irrelevant to particular cases.

3. Phase 2: Filtering and aggregation of prelimi-narily feature set generated at phase 2. The objective here is to compute customer–specific aggregated fea-tures that, independently on the types of their initial measurement scales, are specified as unary predicates with given truth domains and therefore homogeneous.

To explain formally the idea of feature filtering and aggregation, let us introduce the following denotations:

Phase 4

Phase 3

Phase 2

Customer–related data set repre-sented as multi–dimensional (1–0..*) data base in terms of cus-tomer–independent feature sets

Expert–based formation of customer –independent

expanded feature sets

Phase 1

Development of rich domain

ontology

Data transfor-mation proce-

dure 1

Historical cus-tomer–related

data set

Domain on-tology

Data transformation procedure 2

Bayesian Network-based cause–consequence rule extraction (Final step of Recommendation System learning)

Customer –centered ag-gregated fea-

ture set

Customer –centered feature aggregation and filtering proce-

dure

Historical customer –related data set represented as a binary flat table in terms of customer –centered aggregated feature sets

that are binary predicates

Data transfor-mation proce-

dure 3

Recommendation

Legend: Procedures Data

Figure 1. Context–based personalized recommending system technology

Personalized RS – Decision making component

Ensemble of classifiers

Decision fusion procedure

Transfor-mation:

Phases 1–3

Newcase

Customer –related data set represented as object data base (output of the

first phase)

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R ={ mrr ,...,1 }–the set of possible recommendations, e.g. R ={1,…,5}, where “5” stands for “highly rec-ommended” and “1”–for “not recommended”;X ={ nXX ,...,1 }– the set of features identifiers; i

sx –

value of iX , and i – its domain, i.e. isx i . Let

us note that cardinality of any feature iX may be huge. E.g., categorical feature “Movie key male role” can take thousands values, names of actors. Let also be

the customer’s data sample represented in object DB. For a value i

sx i of the feature iX , sample

and recommendation kr , and aggregate i ( kr ):isx i ( kr ) if and only if for vr R ,

v k : /( krp isx )> /( vrp i

sx )+ ,(1)

where is a positive value defining a threshold. The inequality (1) states that conditional probabil-

ity /( krp isx ) of the fact that recommendation kr

should be selected when iX = isx is larger than the

same conditional probability of any other recommenda-tions. Thus, aggregate i ( kr ), is computed via check-

ing (1) for isx i , vr R , v k , whereas

the sample is used to find the probability estimates. Finally, let us introduce unary predicates )( ki rB

that take value “true” if and only if isx i ( kr ), and

“false”, otherwise. These predicates form new binary features specifying customer’s profile. Using inequality (1) and the predicates )( ki rB definition, sample is

then transformed to the set of samples )( 1rS ,…, )( mrStransforming to the terms of truth values of )( ki rB .

The authors’ experience showed that the inequality (1) filters many features of the set { nXX ,...,1 } when they are not satisfied (1) for any kr R . In fact, ag-gregation and filtering are realized, at phase 3, by the procedure (1). The resulting feature space { )( ki rB } ki mn

ki,

1,1 is customer–centered feature space.

4. Phase 4: Cause –consequence rule extraction. Its objective is to find "strong" cause–consequence de-pendencies (rules) between conjunctions of predicates

)( vk rB { )( ki rB } ki mnki

,1,1 and class label Rrj .

Original technology is developed for solution of this task, but due to lack of the paper space it is not consid-ered here. Its outline can be found in [4].

3. Practical Reasoning case study: Personalized e-mail assistant

E–mail assistant objective is to recommend, to the user, into which folder new e–mail to put. E–mails contained in the user’s structured folders and contact list can be utilized as Historical data. RS quality de-pends on how context is extracted from available data and how learning is done. Below the first task is out-lined.

EMailItem

EntryID 000000003465C1F6148B1C40 932E6C94E9F0490224D52100

Size 477097Importance 1BodyFormat 2Conversation Topic KelwinMag Reseller Agree-

ment Read Receipt Request false ReceivedTime 38567.903020833335CreationTime 38567.905411597225 FolderID 240

Bod

y C

onte

nt

George, As promised here is the “Redlined” agreement. Let us know what you can and can’t live with in here. Thanks, Jonathan Kinsbery Effective Solutions eTrade Technology, Inc. 123-456-7890-Wireless 198-765-4321- Fax 654-312-7119- Office "http://www.etrade.com/web/main" eTrade Effective Solutions ____From: Olga Ginsberger Sent: Tuesday, August 03, 2009 11:47 AM To: Jonathan Kinsbery Cc: David Arnold Subject: KelwinMag Reseller Agreement Jonathan – Enclosed please find the redlined KelwinMag Resel-ler Agreement. Would you please forward it to your contact at KelwinMag for their review. Many thanks. Olga Ginsberger Sr. Contracts Administrator eTrade Technology, inc. 12531 DullesDrive, Mail Stop #11 Herndon, VA 12131 Tel: 713-914-3042, Fax: 705-981-8362 ginsberger@etrade.com

Subject FW: KelwinMag Reseller Agreement Attachment FileName KelwinMag Reseller Agreement

03-1-2009.doc AttachmentType Doc

EMailSender m_sEmail jkinsbery@etrade.com

Person FirstName JonathanLastName Kinsbery

EMailReceiver m_sEmail georget@kelwinmag.com

EMailDomain m_sEMailDomain kelwinmag.com m_sEMailDomain etrade.com

Figure 2. Example of e–mail.

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Fig. 2 shows an e–mail example. One can see how diverse available information is: it includes e-mail for-mal properties (importance, sender and receiver, sub-ject, etc.), text of e–mail body with previous re–writing associated with e–mail. A lot of useful information can be got indirectly via joint analysis of these e–mail texts and contact list associated data with information about people/companies met in e–mail body, etc. Let us dem-onstrate the phases 1–3 in this case study.

Phase 1 is to design detailed user–independent do-main ontology specifying meta–level of knowledge of MS Outlook e-mail assistant. Ontology comprises two parts. The first part includes concepts specifying e–mails located in the folders (concept EMailItem of the ontology) and concepts specifying content of user Contact list. UML diagram of this ontology is done in Fig. 3. The user’s name Person (e–mail holder) is ba-sic concept of both ontology components. Person may occupy a Position in a Company or several Positions in different companies, i.e. this relation is of 0..1 – 0..* cardinality. Company has own postal address PostAd-dress, phone number Phone, web domain WEBDomainand web address WebURL that are indirect properties of e-mail holder Person too. Person is also specified by InstantMessengerUIN list representing addresses (log-ins) of personal messengers and nicknames of Personin social networks, e.g. address in Skype, ICQ, QIP, Jahoo-messenger, nickname in Facebook, Live-Journal, etc. Every element of Person’s contact list Contact, in turn, can be connected to the concept Per-son. Therefore, the people mentioned in Person’s con-tact list Contacts are described by same data structure as Person specifying E--mail holder, i.e. by Position, etc. Thus, all the data associated with contact list Con-tact should be imported in object DB described above.

Each EMailItem concept instance is assigned at-tributes. Among them, some are “formal” specifying

relations between EmailItem and Email. The e-mail properties associated with the e–mail body BodyCon-tent specify most informative “informal” properties of EMailItem that is the main source of contextual infor-mation. Specific attribute of e-mail instance is the name of folder in which it is posted. For new e–mail, this attribute is unknown: it is the subject of recom-mendation of e–mail assistant. E–mail is also described by its subject EMailSubject, it may be attached file(s) Attachment having attribute AttachmentType.

Other relations (Fig. 3) can also contain contextual information. They can be formalized in terms of sec-ondary concepts specifying both the e–mail body of EmailItem instance and contact list Contact data (send-ers, CC–addresses, etc.). These concepts are to be in-cluded in the ontology too. This ontology component is shown in Fig.4 as UML diagram. E.g., the name of a company may become a secondary concept if its phone number is met in the e–mail body. In such a case, the company name is considered as a concept connected to the e–mail body. Some of them are auxiliary (e.g. Re-lateditems of BodyContent, EMailSubject, etc.), while other are to be involved in learning process (e.g. keywords, e–mail addresses, phone numbers, instances of InstantMessengerUIN, people’s/company names, etc.).

Specific technology of text analysis and mining was used for extraction of secondary concept from the e–mail body text. It is intended to extract and interpret e–mail context and then to use it in order to automati-cally instantiate the primary and secondary features appearing in the e-mail. This technology was devel-oped and implemented as a component of MS Outlook assistant.

Text analysis module used in Outlook plugin ex-ploits IBM tools: IBM Language Resource Ware(LRW) [5] and IBM Ontological Network Miner(ONM) [6] available at IBM’s alphaworks site [13].

Figure 3. Domain ontology of e–mail assistant.

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Mechanism of regular expressions is used to extract web and e–mail addresses. LRW is used for processing of natural language texts. LRW includes several JAVA libraries realizing functions like language identifica-tion, text segmentation/tokenization, normalization, search for and extraction of entities and relations, se-mantic analysis and disambiguation. In particular, the following LRW capabilities were used:

1. Dictionary–Based annotation. In text analyzer, dictionaries of people and company names are used.

2. Rule based annotation. It segments the text into separate words, sentences and paragraphs and forms text fragments using introduced rules. E.g., for a per-son Barry White the word Barry is annotated as FirstName using people names dictionary and White as a word. The rule ”if FirstName with proper noun next then FullPersonName” annotates Barry White as Full-PersonName. If text processing pipeline is formed, LRW may create/deploy UIMA [14]–compliant anno-tators to use the text analysis pipeline in applications.

For more sophisticated recommendations, ONM [6] was also used to extract key concepts from the text (text focus), even those that are not presented explicitly in the text. ONM also disambiguates concepts.

Ontology for text analysis and mining is developed by expert and specified in XML. Text analysis is done in two stages: extraction of concepts (met in the text) using dictionary and sliding windows and detecting key concepts using semantic dictionary. The ontology is formed using categories and concepts of Wikipedia.

The phase 2 of the technology (section 2) is feature selection. It consists of two stages. At the first stage, experts include in feature set those ones that seems to be useful, clearly understood and interpretable in terms of ontology concepts/attributes. Features selected at this phase are user–independent. Personalization of the features is done at the phase 3 (Fig.1). In it, the fea-tures selected at the first step are aggregated and fil-tered using inequality (1) and object DB recording his-

tory of the user activity. At the phase 4, user–related cause–consequence dependencies are extracted.

The case study features are divided into two groups. The first one includes formal features. Exam-ples of such aggregated features are E–mail size (nu-meric), E–mail sender, Sender contact (both categori-cal), etc. The second group comprises secondary fea-tures. For secondary features, a new type is introduced, Pair–wise of any measurements. Such features contain two positions assigned two connected concepts. Com-ponents of this pair can be categorical, ordered, or nu-merical. In the case study, two sets of concepts form-ing such features are used: Connected notions of e-mail subject and Connected notions of e–mail body. The pair first position can be occupied by such concepts: –Proper noun, –Phone number –Contact name, –E–mail address, –Company name, – Messenger, –E–mail domain, –Web address, –Position, etc.

Examples of secondary features are contact(categorical), Connected notions of e-mail subject,Connected notions of e–mail body (both of pair–wise type), attachment type (categorical), etc. Examples of instances of the secondary features are the following:

Features: connected concepts of e-mail subject (Company name: KelwinMag)Features: Connected notions of e–mail body(Position in company: contracts administrator)(Companies: effective solutions)(Phone numbers: 123-456-7890, 198-765-4321, 654-312-7119, 713-914-3042, 705-981-8362), (E-mail address: ginsberger@etrade.com),(Web address: http://www.etrade.com/web/main), (Proper noun: August), (Proper noun: eTrade),(Contact: Jonathan Kinsbery, Olga Ginsbergr, Da-vid Arnold).

4. Experimental results

For validation, a real–life user e-mail profile with e–mails in profile folders were used. The learning data were divided into two samples, training and testing. Experiment settings were as follows: 1. Attributes of aggregation and filtering algorithm.For categorical data aggregates (both single and pair–wise) the threshold value =0,4 was used in (1). Ag-gregates for numerical features are formed using modi-fied Quinlan measure [10] with numerical domain quantized into 10 unequal subintervals. 2. Setting of Bayesian Network–based cause–consequence rule extraction mechanism. Cause–consequence rules are formed as no more than three literal conjunctions of predicates. The rules “pro” and “cons” were extracted. For each class of recommenda-tion, 30 best rules were selected. Figure 4. Ontology of secondary features

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Table 1. Testing results (over testing sample)1. Accuracy averaged over the set of folders

Coverage False Alarm Refusal Probabilities 0,75 0,0833 0,167 Number of e–mails 9 1 2 2. Accuracy related to particular folders Folders’ ID

4 1 0 0 5 1 0 0 7 0,5 0 0,5 12 1 0 0 13 0 0 1 14 1 0 0 20 0,5 0,5 0

3. Decision making. As a decision making mechanism, a variant of combining of decision of classifier ensem-ble was used, where the cause–consequence rules play the roles of classifier ensemble.

The results are given in Tab. 1. Let us note that ex-perimental data corresponds to real–life cases of e–mails, were data are rather complex and no simplifica-tions were done. Experimental results are promising. Indeed, analysis of the results (Tab. 1) shows that, in general, technology works well for the most folders, although recommendations produced in regard to fold-ers 5 and 20 are rather bad. This fact does not falsify the technology. Analysis of particular e-mails for which the produced recommendation are bad should prompt the needed ontology corrections and, perhaps, the necessary extension of the feature set. With in-creasing of the learning sample the accuracy should also be improved.

5. Conclusion

The paper contribution is a core technology and re-usable software engine for rapid design of a broad range of applications in the field of personalized rec-ommendation systems and more. The proposed core technology novelties are threefold: (1) enrichment of historical data with expert knowledge and their trans-formation to object DB form that makes it possible to take into account context and semantics of available data both at learning and RS operation phases; (2) au-tomatic procedure of informative feature aggregation, selection and filtering; and (3) technique for transfor-mation of source heterogeneous data to homogeneous binary data form that reduce learning of decision mak-ing component to conventional binary learning task.

The developed technology is fully implemented and validated based on several applications. Among

them, intelligent e–mail assistant, movie recommend-ing system based on NetFlix data set, wine recom-mending system and some other.

Further research is to enrich text analysis and min-ing technology, achievement more reusability of the software and technology further validation on more wide range of applications.

References

1. Aggregate Knowledge Discovery Platform - Dynamic Creative. http://www.aggregateknowledge .com/

2. Amadesa Customer Exper. Suite.http://www.amadesa.com

3. enRICH Personalization engine.http://www.richrelevance.com/

4. V. Gorodetsky and V. Samoylov. Feature Extraction for Machine Learning: Logic-Probabilistic Approach JMLR Workshop and Conference Proceedings, vol.10, 2010, pp. 55-65.

5. IBM Language Resource Ware. http://www. lpha-works.ibm.com/tech/lrw.

6. IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks, http://www.alphaworks.ibm.com/tech/ galaxy

7. S. Jean, Y. Aït-Ameur, and G. Pierra, Querying Ontology Based Database Using OntoQL (An Ontology Query Lan-guage). LNCS, Vol. 4275, Springer, pp.704-721.

8. Linden, G., B. Smith, and J. York. Amazon.com Recom-mendations: Item-to-Item Collaborative Filtering. IEEE In-ternet Computing, January–Febryary, 2003.

9. NetFliX. http://www.netflix.com.

10. Quinlan J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA, 1993.

11. Sematext Recommendation Engine. http://www.sematext.com/products/recommendation-engine/index.html

12. SUGGEST: Recommendation engine. http://glaros.dtc.umn.edu/gkhome/suggest/overview.

13. Text Analytics Tools and Runtime for IBM Language-Ware, http://www.alphaworks.ibm.com/tech/lrw.

14. Unstructured Information Management Architecture. http://www.info-srv.ru/content/view/50/31/.

15. Xiam Technologies. http://www.xiam.com/

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