Learning To Select Historical News Articles For Interaction Based Neural News Recommendation

Though some recent “multi-channel” methods (Liu et al., 2019; Chen et al., 2018) attempt to cover richer information by maintaining multiple representation vectors, they are still limited to modeling fine-grained interaction in an explicit and reliable manner. In order to capture fine-grained matching signals between the candidate news and the user, Wang et al. Wang et al., 2020) proposed an interaction-based model. It computes similarity matrices between the candidate news. Every historical news piece of the user at word level to derive the click probability. Despite the effectiveness improvement, the model is especially slow. It has to recompute term-level interaction matrices with every historical article when scoring each candidate, which is far more expensive than dot product used in representation-based methods. Intuitively, we shouldn’t involve all the historical articles in interaction. Tailoring the user history to several recent browsed news items seems to be a straightforward solution to save efficiency. However, blindly interacting with only the latest browsed news limits the recommendation effectiveness, mainly because: 1) When the length of the kept browsing history is short, there isn’t sufficient news for the model to learn the user’s interests well.

This is how good copywriting works. Marketers need to express their brand and products or services aptly through a proper communication tool in revealing the audience’s emotions and needs. When readers of the business message connect or relate to the contents, they would put their trust and hope in the marketer’s recommendation for the desired solution. Good relations could be established between the potential leads. The marketer who would move closer to making the sale. Basically, there are two types of copies which a marketer could indulge in: long copy and short copy. A long copy, as the term implies, is a longer version of a strong marketing article which has the ability to generate higher sales than its short counterpart. This would only happen if the long copy is well written by the author who is focused and well versed with the copy objective; otherwise, a long copy could be a disaster. A long copy allows the copywriter more time and space to convey the appropriate message to the reader, whereas the short copy offers fewer words to achieve the same purpose.

That's where the robots come in. Sometimes, the best offense really is a good defense. It's no wonder, then, that major militaries have devoted so much time and money to developing effective camouflage. The enemy can't hit what it can't see. For the battlefield of the future, the U.S. Army is developing an infantry uniform that will provide superhuman strength, advanced communications and greater ballistic protection than any uniform to date. Body armor can't work miracles, but it can greatly improve a soldier's chances on the battlefield. This article explains how modern body armor stops bullets in their tracks. Why a shotgun instead of, say, a rifle? Well, to do its job, a projectile must both make contact with the target and hit the target in a critical spot. With a wider stream of potentially deadly projectiles, a shotgun is like using a can of spray paint if a rifle is like using a felt-tip pen.

We design our LamBERTa framework to solve such issues based on different schemes of unsupervised training-instance labeling that we originally define for the ICC corpus, although they can easily be generalized to other law code systems. We address one crucial aspect that typically arises in deep/machine learning models, namely explainability, which is clearly of interest also in artificial intelligence and law (e.g., BrantingWBPCFPY19 ; HackerKGN20 ). In this regard, we investigate explainability of our LamBERTa models focusing on the understanding of how they form complex relationships between the textual tokens. We further provide insights into the patterns generated by LamBERTa models through a visual exploratory analysis of the learned representation embeddings. The obtained results have shown the effectiveness of LamBERTa, and its superiority against (i) widely used deep-learning text classifiers that have been tested on our different query sets for the article prediction tasks, and against (ii) a few-shot learner conceived for an attribute-aware prediction task that we have newly designed based on the availability of ICC metadata.

The Crimson Tide defeated the Aggies 45-23. (Wesley Hitt/Getty Images) Then what happened? The NCAA’s Berlin Wall collapsed. Virtually overnight we’ve gone from a regulated communistic state behind the Berlin Wall to (mostly) unregulated capitalism when the wall came tumbling down. Whereas before the NCAA enforcement agents patrolled college athletics to ensure no one received “improper benefits,” a totally made up communistic phrase if ever there was one (what the hell even is an “improper benefit?”) now we have players effectively selling their labor on the open market. There was no middle ground here, we just flipped from one governance structure to another with virtually no middle ground or transition period. Premium official Mark Prior Forever Shirt in other words I will buy this money was restricted from being spent on the most important assets - players. So under our old system college coaches could make $10 million a year - remember, some communists are always really rich - and college weight rooms had waterfalls, but players, the average citizen, was restricted in profiting off their labors. Ironically some of the foremost proponents of capitalism, red state living Republican voting fans, are the most upset by college communism ending. To a large degree I suspect that’s because many are just uncomfortable with change. Haven’t spent much time thinking about what they actually support. So if you’re one of these people, I’d just ask you this, why do you want to make as much money as possible in your life and not want that for other people?