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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

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

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  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

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