**jm + heuristics**
2

[1907.06902] _Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches_

(via Halvar Flake)
via:halvarflake
deep-learning
machine-learning
ml
papers
algorithms
top-n
heuristics

29 days ago by jm

Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in general, it has, as a result, become difficult to keep track of what represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks. At the same time, several recent publications point out problems in today's research practice in applied machine learning, e.g., in terms of the reproducibility of the results or the choice of the baselines when proposing new models. In this work, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks. Specifically, we considered 18 algorithms that were presented at top-level research conferences in the last years. Only 7 of them could be reproduced with reasonable effort. For these methods, it however turned out that 6 of them can often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or graph-based techniques. The remaining one clearly outperformed the baselines but did not consistently outperform a well-tuned non-neural linear ranking method. Overall, our work sheds light on a number of potential problems in today's machine learning scholarship and calls for improved scientific practices in this area.

(via Halvar Flake)

29 days ago by jm

Bug Prediction at Google

march 2015 by jm

LOL. grepping commit logs for /bug|fix/ does the job, apparently:

bugs
rahman-algorithm
heuristics
source-code-analysis
coding
algorithms
google
static-code-analysis
version-control
In the literature, Rahman et al. found that a very cheap algorithm actually performs almost as well as some very expensive bug-prediction algorithms. They found that simply ranking files by the number of times they've been changed with a bug-fixing commit (i.e. a commit which fixes a bug) will find the hot spots in a code base. Simple! This matches our intuition: if a file keeps requiring bug-fixes, it must be a hot spot because developers are clearly struggling with it.

march 2015 by jm

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