personalized recommender systems in e-commerce and m-commerce: a comparative study
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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 - PowerPoint PPT PresentationTRANSCRIPT
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www.umbc.edu
Personalized Recommender Personalized Recommender Systems in e-Commerce and m-Systems in e-Commerce and m-
Commerce: A Comparative StudyCommerce: A Comparative Study
Azene Zenebe, Ant Ozok and Anthony F. NorcioDepartment of Information Systems
University of Maryland Baltimore County (UMBC)Baltimore, MD 21250 USA
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OutlineOutline
• 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
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IntroductionIntroduction• 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
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A summary of comparison between e-A summary of comparison between e-commerce and m-commercecommerce and m-commerce
Factor E-Commerce M-Commerce
Technology Device PC Smartphones, Pagers, PDAs, Cell phones
Operating System Windows, Unix, Linux Symbian (EPOC), PalmOS, Pocket PC, proprietary platforms.
Common Communication protocols in m-commerce are
Web’s Hyper Text Transfer Protocol (HTTP)
Wireless Application Protocol (WAP) and DoCoMo”s (Japan) proprietary protocol
Programming and presentation Standards
HTML, XML, JavaScript, Java, etc.
HTML, WML, HDML, i-Mode, Java support
Browser Microsoft Explorer, Netscape
Phone.com UP Browser, Nokia browser, MS Mobile Explorer and other micro-browsers
Bearer Networks TCP/IP & Fixed Wired-line Internet
GSM, GSM/GPRS, TDMA, CDMA, CDPD, paging, Wireless Fidelity (Wi-Fi) networks
Services Personalized Recommendation Well Developed Not Well Developed as e-commerce except a few location-based systems ???; Begins via wired Internet
Accessibility At desktop, workstation, etc.
Ubiquitous: Any time and anywhere
Customer Usage Motivation if they have good reasons or not
Only if they have good reasons
Usability relatively good number of studies
very few studies
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Personalized Recommender Systems - Personalized Recommender Systems -
FrameworkFramework 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
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Personalized Recommender Personalized Recommender Systems - ExamplesSystems - 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)
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Personalized Recommender Personalized Recommender Systems - ExamplesSystems - 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)
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Personalized Recommender Personalized Recommender Systems – Current StatusSystems – 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?)
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ComparisonComparison• 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
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Customer & Product ModelCustomer & 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
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I/O and Interaction 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.
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I/O and Interaction 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
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Methods and Algorithms 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
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Methods and Algorithms 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
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Confidence/Uncertainty and Confidence/Uncertainty and ExplanationExplanation
• 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
• Explanation on how are the recommendation obtained?– creating an accurate mental model of the
recommender system and its process
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Confidence/UncertaintyConfidence/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.
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Results of Evaluation 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
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ConclusionConclusion
• 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
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Future ResearchFuture 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
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www.umbc.edu
Appendix IAppendix I
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• FTMax-best and FTMin-worst from Fuzzy Theoretic Approach• CMMax-best and CMMin-worst results from conventional approach
P R F1
CMMin 0.220 0.131 0.120
CMMax 0.220 0.271 0.240
FTMin 0.509 0.199 0.239
FTMax 0.527 0.284 0.316