1 / 28

Enhancing User Experience through Knowledge-Based Personalization in InfoQuilt

This paper delves into the concept of knowledge-based personalization within the InfoQuilt system, leveraging semantic web technologies. It discusses the architecture of the InfoQuilt system, focusing on the personalization agent and the representation of user profiles through ontologies. Various personalization techniques are analyzed, including keyword matching and query relationships that enhance user engagement. The paper also compares related works in the field of personalization and outlines future directions for improving the personalization algorithms and ontology relationships to better serve user interests.

elizabeth-kirk
Télécharger la présentation

Enhancing User Experience through Knowledge-Based Personalization in InfoQuilt

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Knowledge based Personalization by Wonjung Kim

  2. Outline • Introduction • Background – InfoQuilt system • Personalization in InfoQuilt • Related Work • Conclusions and Future Work

  3. Introduction • Semantic web - components • Semantics of data • Semantics of human’s interest • Personalization is a part of the second component

  4. Background – the InfoQuilt system • Semantics based information processing • IScape : Information correlation • Knowledge sharing based on multiple ontologies

  5. Background – Overall Architecture server

  6. Background – Architecture of a Peer Personalized Knowledge Base Personalization Agent IScape Execution

  7. Background – Personalized Knowledge Base Shared ontologies Personalized ontologies

  8. Personalization – in InfoQuilt system • Representation of user profiles • Personalization Techniques • Personalization Algorithm • Examples

  9. Representation of user profiles • Set of tuples of type <Keyword, Ontology, Frequency, Latest interest, IScape> • Keyword: the term used to query • Ontology: used in IScape • Frequency: frequency of query • Latest interest: boolean value • IScape: the name of the last queried IScape

  10. Personalization Techniques Score can be computed based on a scale of 0..1 • Keywords matched • Profiles matched • Knowledge about latest context • Frequency of querying a domain • Query relationship • Distance from a domain of interest

  11. Personalization Techniques- Query relationships • More concrete than e-commerce market association rules • Buy Cereal  Buy Milk • Query Relationship • if a bulldog football team has a game scheduled, then the user may be interested in attending the game so he may query for flight ticket and vice versa. • Use framework for inter-ontological relationships to define query relationships • spatiallyNear(UGAFootball.gameVenue, Flight.arrivalCity) && temporallyNear(UGAFootball.gameDate, Flight.arrivalDate)

  12. Personalization Techniques- Query relationships • Query Relationships: • Flight  UGAFootball, Flight  UGABasketball • Query: “bulldog schedule”

  13. Personalization Algorithm

  14. Personalization Algorithm These weights are configurable

  15. Examples Personalized Knowledge Base

  16. Example 1 – without profile information (first Query)

  17. Example 1 – keyword matching

  18. Example 2 – use of user profile P1 <bulldogs, UGAFootball, 2, true, Iscape1> Query: “bulldogs”

  19. Example 3 – latest context P1 <bulldogs, UGAFootball, 10, false, Iscape1> P2 <bulldogs, UGABasketball, 2, true, Iscape2> Query: “bulldogs”

  20. Example 4 – query relationship

  21. Example 5 – new query term P1 <bulldogs, UGAFootball, 12, false, Iscape1> P2 <bulldogs, UGABasketball, 10, true, Iscape2> P3 <travel, AirTravel, 2, true, Iscape3> Query: “gamecocks”

  22. Related Work • Features of Knowledge Based personalization in InfoQuilt not supported by any other personalization systems • Keywords and concepts in ontologies are used to locate them • Query relationships between domains identify domains that the user’s profile provides no information for

  23. Related Work… • OBIWAN ( Alexander P, Susan G) • Use a vector space model to classify documents • use length, time, and the strength of match to track users’ interest • myPlanet (Yannis K, John D, Enrico M, Maria V, Simon S) • An ontology-driven personalized news publishing service • Use simple relationships in the ontologies to deliver content that may be of interest to the user

  24. Related Work… • Scalable online personalization on the web (Anindya D, Kaushik D, Debra V, Krithi R, Shamkant N) • Collaborative filtering approach • Action rules and market basket rules • Dynamic profile

  25. Conclusion • Personalization in InfoQuilt • Ontologies in the personalized knowledge base reflect the user’s perception of the domain • Keywords that are specified by the ontology, are useful for identifying other relevant ontologies • A number of techniques combined to help the users find relevant ontologies • Query relationships can identify related domains of interest in the current context of user’s query

  26. Future Work • For each domain, it is possible to identify a set of terms that indicate the context. These can also be used to locate ontologies. • The only type of relationships in the ontologies used for identifying domains that may be of interest to the user is “is-a”. We can explore the user of other types of relationships supported by ontologies • Evaluating query relationships requires work equivalent to evaluating one IScape. Instead, the results from the previous IScape can be cached.

  27. Future Work • Keyword matching can be further given weights depending on which component of ontology the keyword matched. For example, if a keyword matches the name of a class as opposed to description, it should have higher value. • Experimenting with large amount of users and ontologies can help in identifying a reasonable weight assignment for the techniques.

  28. Thank You!

More Related