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 USA

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

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

A summary of comparison between e- commerce and m-commerce A summary of comparison between e- commerce and m-commerce FactorE-CommerceM-Commerce TechnologyDevicePCSmartphones, Pagers, PDAs, Cell phones Operating SystemWindows, Unix, LinuxSymbian (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 BrowserMicrosoft Explorer, Netscape Phone.com UP Browser, Nokia browser, MS Mobile Explorer and other micro- browsers Bearer NetworksTCP/IP & Fixed Wired- line Internet GSM, GSM/GPRS, TDMA, CDMA, CDPD, paging, Wireless Fidelity (Wi-Fi) networks ServicesPersonalized RecommendationWell DevelopedNot Well Developed as e-commerce except a few location-based systems ???; Begins via wired Internet AccessibilityAt desktop, workstation, etc. Ubiquitous: Any time and anywhere Customer Usage Motivationif they have good reasons or not Only if they have good reasons Usabilityrelatively good number of studies very few studies

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

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)

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)

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?)

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

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

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.

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

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

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

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 Explanation on how are the recommendation obtained? –creating an accurate mental model of the recommender system and its process

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.

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

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

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

Appendix I

FTMax-best and FTMin-worst from Fuzzy Theoretic Approach CMMax-best and CMMin-worst results from conventional approach PRF1 CMMin CMMax FTMin FTMax