random notes & thoughts
From the Sunday’s workshops, I remember this paper “Dating Sites and the Split-complex Numbers” It uses split-complex numbers to represent dating preferences in an elegant way. It seems promising. I’d be great to connect this work on previous papers on trust and distrust and on structural balance theories… I also heard that two presentations were quite good: 1) Content, Connections, and Context 2) Joseph Konstan talk abt the different decision strategies ppl have in different contexts.
On Thursday, we run a workshops on mobile recommender systems. Francesco Calabrese of IBM Smart Cities gave an interesting invited talk about current projects on transportation systems. Then, we had a set of really good talks & one outdoor activity. What did I learn? Well, most of the existing mobile systems assume that the recommendation process unfolds in one single step – get restaurant recommendations & choose one of them. In reality, recommendations in the built environment should go beyond that. For example,
- To mimic humans, the task of recommending restaurants should at least return 3 different recommendations (or facets): closest restaurant, best restaurant, trade-off between the two.
- One should understand WHY people visit certain places. How did they make those decisions? Which criteria did they employ?
- Recommender systems need to tap into established findings in the area of urban studies. For example, in our RecSys paper “Ads & the City“, we exploited the fact that people are boring – they generally do not travel very far – unless what they are looking for is not readily available where they are.
- Temporal patterns in recommender systems have not been widely studied. They have been studied on Web platforms only recently (and Neal Lathia has done great work on that!) and have been neglected in mobile platforms. That is why we had another paper in the conference titled “Spotting Trends: The Wisdom of the few“
- Finally, and more importantly, we need far more user studies of how these systems are ACTUALLY used! Recommendations do not matter much -the experience counts
And this is just scratching the surface
I remember only few things from the conference (the industry track was pretty good):
- Multiple Objective Optimization in Recommendation Systems (linkedin). Nice example of A/B testing
- Towards Personality-Based Personalization (Thore Graepel of Microsoft Research). Nice talk about how easy is to predict personal attributes of Facebook users based on their likes. if you are interested in personality and social media, you should check out our work on Facebook and Twitter (we can predict personality traits of twitter users upon only their number of followers, following, and listed counts)
- Building Industrial-scale Real-world Recommender Systems (Xavier Amatriain of Netflix). Brilliant (& fully packed) tutorial. Check this out for a summary.
- Controlled experiments at Microsoft Bing (very
- Pareto-efficient hybrization for multi-objective recommender systems (UFMG). Here the question is how to combine different types of algorithms (hybrization).
- User Effort vs. Accuracy in Rating-based Elicitation (PoliMI). What’s the optimal number of users ratings for movie recommendations? It seems to be between 5 to 20.
- TasteWeights: A Visual Interactive Hybrid Recommender System (UCSB). Visualization platform for your social media stream
- Learning to rank optimizing MRR for recommendations. Very cool work. It taps into the less is more concept, which I’m a big fan of
- Thumbs up to real-world stuff: Beyond Lists: Studying the Effect of Different Recommendation Visualizations; Yokie – Explorations in Curated Real-Time Search & Discovery Using Twitter; A System for Twitter User List Curation; The Demonstration of the Reviewer’s Assistant; CubeThat: News Article Recommender (browser extension for Chrome displays recommended additional news stories related to the same topic as the current news story)
- Challenges in music recommendation (@plamere from @echonest). A couple of interesting insights: “Understanding the specifics of your domain is critical to building a good recommender”; and recommending down-tail is OK, while recommending up-tail (britney to one who likes tom waits) is risky. Might be offensive to one’s music identity. So make your recommendations Hipster-Friendly