1 / 21

Personalized Recommender Systems in e-Commerce and m-Commerce: A Comparative Study

Personalized Recommender Systems in e-Commerce and m-Commerce: A Comparative Study. Azene Zenebe, Ant Ozok and Anthony F. Norcio Department of Information Systems University of Maryland Baltimore County (UMBC) Baltimore, MD 21250 USA. Outline. Introduction m-commerce verse e-commerce

cana
Télécharger la présentation

Personalized Recommender Systems in e-Commerce and m-Commerce: A Comparative Study

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. Personalized Recommender Systems in e-Commerce and m-Commerce: A Comparative Study Azene Zenebe, Ant Ozok and Anthony F. Norcio Department of Information Systems University of Maryland Baltimore County (UMBC) Baltimore, MD 21250 USA

  2. Outline • Introduction • m-commerce verse e-commerce • Personalized recommendations services (PRS) • System Framework • recommender systems of Amazon and MovieLens • Comparison • Factors for comparison • Requirement analysis for PRS for mobile users and devices • Conclusion & Future research

  3. Introduction • E-commerce verse m-commerce • Challenges in m-commerce (Ghinea & Angelides, 2004; Turban, King, Lee, & Viehland, 2004; Nielsen, Molich, Snyder, & Farrell, 2001 ) • limited data or query input capability • limited display capability (2-2.5’),resolution • limited processing speed and memory • customer confidence is still low to cell phone transactions • limited data transmission capability speeds • low battery power of devices • customer confidence is still low

  4. A summary of comparison between e-commerce and m-commerce

  5. Personalized Recommender Systems - Framework • What is a Personalized RS? • matches a customer’s interest, preference, etc. & the products’ attributes • Recommends products or services to customers tailored to their preferences

  6. Personalized Recommender Systems - Examples • e-commerce: • Amazon’s personalized recommendations that recommends books, DVDs, etc., and • MovieLens (Sarwar, Karypis, Konstan, & Riedl, 2000) which is a movie recommender system. • Interested reader can refer (Herlocker, Konstan, Terveen, & Riedl, 2004; Schafer, J, & Riedl, 2001)

  7. Personalized Recommender Systems - Examples • m-commerce: • Amazon Anywhere for Palm PDAs and WAP devices • Research systems: • PocketLens (Miller, Knostant, & Riedl, 2004) • MovieLens Unplugged (Miller, Albert, Lam, Knostant, & Riedl, 2003)

  8. Personalized Recommender Systems – Current Status • Highly successful in e-commerce • M-commerce? • No personalized recommendation service for cell phones users in Amazon for digital access • MovieLens are also not yet fully adapted to mobile access • Challenges in m-commerce (why not matured?)

  9. Comparison • Goal • Elicit additional requirements to adapt the technology developed & advanced in e-commerce RS to m-commerce RS • Factors/Components • Customer/user, product and service model • Recommender engine/algorithms • User interface (I/O and interaction) • Confidence and uncertainty model • Acceptance/Trust

  10. Customer & Product Model • Facts/assumptions about a customer: • personal facets; behavioral facets; cognitive facets • contextual facets-include physical location, past interaction, hardware and software available, tasks, and other users in the environment • Representation of Products’ information • m-commerce: • the contextual facets are more essential for effective and useful recommendation decisions • Concise and easy way of representation of product

  11. I/O and Interaction • Input • individual user's implicit navigation • explicit ratings • purchase history and keywords • comments from community • M-commerce • initially customers have to sign in wired web • location information needs to be gathered using devices like GPS • less opportunity for gathering data during interaction • MovieLens Unplugged (Miller et al., 2003) attempts to provide a link on the mobile device, later found it to be rarely used.

  12. I/O and Interaction • Output • Customers need as much information as possible about a product or service • to get movie synopsis or reviews on movies • To present images, clips, etc. of products • explanations of how those recommendations are generated • M-commerce • Is it feasible to display in effective ways all these outputs in mobile devices’ display? • optimal number of items to be displayed is limited usually in range 1 to 5, • e.g. 4 items in MovieLens Unplugged compared to 10 to 20 items in e-commerce

  13. Methods and Algorithms • Approaches and steps used for • identifying and generating information and assumptions about customers, • recommendations • Content-based or action-based • Amazon Eyes and eBay Personal Shopper (Schafer et al., 2001) • Collaborative Filtering (CF) • User – user CF; Item – item CF • Amazon Your Recommendations • Amazon Customers who Bought • Hybrid • CF - performed offline using a dedicated server

  14. Methods and Algorithms • Algorithms of e-commerce need to be adapted using the input, process and output requirements of mobile users and mobile devices • need to support localization for location-specific recommendations • need to support for updating customer model, and for generating recommender on fly during customer-system interaction

  15. Confidence/Uncertainty and Explanation • Refers to degree of doubt associated in making recommendations for users • the incompleteness, imprecision, vagueness, randomness or ambiguity • Confidence/uncertainty information • level of confidence in user and product model estimates, about the results of inference or reasoning, and in the recommendations • Explanationon how are the recommendation obtained? • creating an accurate mental model of the recommender system and its process

  16. Confidence/Uncertainty • Uncertainty originates from during: • representing interest using crisp values; • representing the product attributes: genre • expressing true relationship among the products as well as users’ preference to products • Proposed a Methodology for PRS using Fuzzy and Possibility theory - fuzzy set membership function • to represent and handle uncertainty that exists in product attributes (e.g. movie genre), user attributes (e.g. ratings) and their relationship in recommender systems.

  17. Results of Evaluation • Simulated Movie Recommender System • Empirical evaluation: • Datasets from MovieLens and IMDb • Compared to best reported results • Results: • Faster • nearly 1/10 seconds to infer a customer’s interest for a movie (model time) • nearly 1/5 seconds to recommend a movie (recommendation time) • Higher precision (increase by 141%), • 3 to 5 recommendations verse 10 • require a few (5 to 10) initial ratings (model size) from a customer verse 10 to 20

  18. Conclusion • Most important dimensions/components • More similarities in the components • Additional requirements for m-commerce • Using fuzzy set and possibility theory for handling uncertainty in e-commerce showed a great potential for m-commerce

  19. Future Research • Implement an actual recommender system to e-commerce and m-commerce customers • Usability study • input and output interfaces of the different mobile devices • Usefulness of explanation and confidence information • Trust

  20. Appendix I

  21. FTMax-best and FTMin-worst from Fuzzy Theoretic Approach • CMMax-best and CMMin-worst results from conventional approach

More Related