Feng Shan, Kristen Vaccaro, Kirstin Phelps CS 598 kgk Fall 2013 Final Class Project

1. Feng Shan, Kristen Vaccaro, Kirstin Phelps CS 598 kgk Fall 2013 Final Class Project

2. Audience Small groups seeking a recommendation for lunch or dinner • 3 - 8 people • Friends, colleagues, co-workers, peers Assumptions: • Same geographical area (limit to Champaign-Urbana) • May be (very) familiar with available dining options

3. Motivation • Small footprint: quick, easy system for food recommendation • Help small group decision-making • Asynchronous and low interaction • Mitigate reviewer rating signals, may lead to automatic dismissal of restaurants with lower ratings • Decrease difficulties in small group decision-making • Influential/highly verbal individual,may lead to herding, hurt/apathetic feelings • Assumptions on food choice with no knowledge • Influence of brand names, may lead to herding

4. Research Questions RQ1: How will a tool focused around characteristics of particular menu items rather than of restaurants, impact user satisfaction? RQ2: Will a tool focused around food characteristics rather than restaurant category, increase novel food choices? RQ3: Does asynchronous, remote decision-making improve overall group satisfaction with restaurant choices? RQ4: What is the level of satisfaction with Yum compared to other applications?

5. Interface Design User preferences for temperature, texture, and flavor

6. Methodology Interface decisions • Online • Abstraction of food characteristics • not focused on reviews or ratings • Minimalistic, simple look • Asynchronous group decision making

7. Methodology Data • Taxonomy of restaurants • Representative sample of six food items • Chosen to span flavor/texture/temp space • Restricted to Urbana Champaign for initial implementation • For full coding, need Mechanical Turk • Inter-rater reliability

8. Methodology Algorithm • Selected to optimize ability to retrieve second and third place results • Inefficient in time and space (but UC has limited # restaurants, efficiency not necessary)

9. Survey Groups:12 Average = 3, Range = 2 - 8 Users: n = 37 Response Rate: 81% 28 item survey captured: • Satisfaction of recommendations • Perception of novelty • Suggested improvements • Etc...

10. Conclusions RQ1: How will a tool focused around characteristics of particular menu items rather than of restaurants, impact user satisfaction? • LOVED to have a recommendation for entrees also and not only for the restaurant…each member of the group knows for sure that there will be a nice fit…also makes deciding on what to order easier… • My first recommendation was a little off, but the others I thought were spot on • They seem to fit what I was hungry for at the time • Particularly the third recommendation met my amorphous expectation when I was choosing flavors and textures.

11. Conclusions RQ2: Will a tool focused around food characteristics rather than restaurant category, increase novel food choices?

12. Conclusions RQ2: Will a tool focused around food characteristics rather than restaurant category, increase novel food choices? • I would never have thought of at least one of my options, but it turned out that that one sounded the most appealing. • I would probably never choose Lentil soup as something I'd eat, and it doesn't sound particularly appealing… • This allows options to go somewhere and try something specific...versus trying to agree on the restaurant itself and not knowing what you might want on the menu. • It could also give surprises on suggestions. I like to try new things!

13. Conclusions RQ3: Does asynchronous, remote decision making improve overall group satisfaction with restaurant choices.

14. Conclusions RQ3: Does asynchronous, remote decision making improve overall group satisfaction with restaurant choices. • Yum was good for helping the group make a decision...particularly a group that might know each other but might not choose to go out together on a date or socially...like a work group. For a date or something with my partner, I would probably use Yelp just because of the additional information and my partner and I are more aligned in our flexibility and love of trying new things. We are more willing to take a risk. • I do like it's simplicity though. It makes the group-decision aspect way easier by taking minutia out of people's options. • Easier to make a decision, first few restaurants have been pre-selected for discussion. • It was super fast for the decision making!

15. Of 77% of users who use other restaurant recommenders: 40% think the experience with Yum was similar to other systems 60% think the experience with Yum was betterthan other systems No one thought it was worse Conclusions RQ4: What is the level of satisfaction with this application compared to others?

16. A lot of times it is difficult even to decide which type of cuisine we want to have, so using Yelp or Urban Spoon doesn't really help Even google doesn't have a good way to tell me where I can find 'a food that corresponds to a cold, rainy afternoon' but your app implicitly does. Yum recommends restaurants for group and also the entrees that best fit to individual preference in the group…I have not seen any application doing both. Conclusions RQ4: What is the level of satisfaction with this application compared to others?

17. Account for additional features: Distance (some restaurants too far away) Will the restaurant accommodate the group size? Dietary restrictions (vegetarian, vegan) Preferences (eg. not fond of eggplant) I hope this is a program that becomes implemented more because it is a great way to find the perfect food for the taste that you want. Brings a new concept to choosing a restaurant, that I had not thought of... I would give it a try and consider the restaurants it recommends. Conclusions Additional feedback