Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history. The lack of historical ratings results in the user and the item cold-start problems. The latter is the main focus of this work. Most of the current literature addresses this problem by integrating content-based recommendation techniques to model the new item. However, in many cases such content is not available, and the question arises is whether this problem can be mitigated using CF techniques only. We formalize this problem as an optimization problem: given a new item, a pool of available users, and a budget constraint, select which users to assign with the task of rating the new item in order to minimize the prediction error of our model. We show that the objective function is monotone-supermodular, and propose efficient optimal design based algorithms that attain an approximation to its optimum. Our findings are verified by an empirical study using the Netflix dataset, where the proposed algorithms outperform several baselines for the problem at hand.Comment: 11 pages, 2 figure

Distributed Exploration in Multi-Armed Bandits

We study exploration in Multi-Armed Bandits in a setting where $k$ players collaborate in order to identify an $\epsilon$-optimal arm. Our motivation comes from recent employment of bandit algorithms in computationally intensive, large-scale applications. Our results demonstrate a non-trivial tradeoff between the number of arm pulls required by each of the players, and the amount of communication between them. In particular, our main result shows that by allowing the $k$ players to communicate only once, they are able to learn $\sqrt{k}$ times faster than a single player. That is, distributing learning to $k$ players gives rise to a factor $\sqrt{k}$ parallel speed-up. We complement this result with a lower bound showing this is in general the best possible. On the other extreme, we present an algorithm that achieves the ideal factor $k$ speed-up in learning performance, with communication only logarithmic in $1/\epsilon$

SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems

We study combinatorial problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and planning. This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e.g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process. Our solution is quite generic, scalable, and leverages distributional knowledge of the problem parameters. We frame the solution process as an MDP, and take a Deep Q-Learning approach wherein states are represented as graphs, thereby allowing our trained policies to deal with arbitrary changes in a principled manner. Though learned policies work well in expectation, small deviations can have substantial negative effects in combinatorial settings. We mitigate these drawbacks by employing our graph-convolutional policies as non-optimal heuristics in a compatible search algorithm, Monte Carlo Tree Search, to significantly improve overall performance. We demonstrate our method on two problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our method outperforms custom-tailored mathematical solvers, state of the art learning-based algorithms, and common heuristics, both in computation time and performance

Lives on the Line: The Online Lives of Girls and Women With and Without a Lifetime Eating Disorder Diagnosis

This study aimed to compare the scope, internet use patterns, and degree of online need satisfaction of girls and women with and without a lifetime eating disorder (ED) diagnosis. Participants were 122 females aged 12–30, 53 with a lifetime ED diagnosis recruited via a hospital-based treatment program, and 69 age-matched controls recruited via normative social media sites. Participants completed questionnaires assessing disordered eating, body image, positive and negative affect, general distress, and life satisfaction, and completed an online survey about the scope of their internet use, the frequency of watching and posting pictures and videos, online friendships and social comparison, fulfillment of needs online, and mood after internet use. All questionnaire scores differed significantly between groups in the expected directions. Whereas overall, ED and control groups spent similar amounts of time online (6.21, SD = 5.13), they spent this time differently. ED participants reported devoting 56.7% of their online time to eating, weight and body image, versus 29.1% for controls, and spent significantly more time than controls on forums and blogs (t = -5.3, p < 0.0001, Cohen’s d = 0.87). They also engaged more often in social comparison (t = 3.6, p < 0.005, Cohen’s d = 0.65), had a higher online–offline friend ratio (t = 3.7, p < 0.0001, Cohen’s d = 0.65), and more online friends with ED (t = 5.4, p < 0.0001, Cohen’s d = 0.89). In comparison to controls, ED participants reported that their use of forums and blogs gave them more eating- and weight-related advice, and a greater sense of belonging, social support, and safety resulting from anonymity, with effect sizes of 0.63–0.96. However, they also reported more negative affect after posting online. Most online behaviors and patterns correlated positively with measures of symptomatology and negatively with measures of psychological health, in both groups. Internet use was rarely addressed in therapy. Professionals, families and friends should help people with disordered eating and EDs to broaden the scope of their internet use. They should invest less in food- and weight-related forums/blogs, expand their “real life” social lives and develop their interpersonal skills, so that their legitimate needs can be satisfied face-to-face, rather than virtually. Clinicians should address the online lives of their ED clients in therapy

Fast Coding of Orientation in Primary Visual Cortex

Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple ‘race to threshold’ readout mechanism to quantify the information content of spike time latency of primary visual (V1) cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made

Supplement 1: Ptychographic reconstruction algorithm for frequency-resolved optical gating: super-resolution and supreme robustness

supplementary material Originally published in Optica on 20 December 2016 (optica-3-12-1320