Search and find board game

The eternal dilemma. For the purpose of this exercise, they do. Netrunner is the cyberpunky card game our tech desk seem to play a lot of their time playing. There can be no greater endorsement. Eye-rollers, move on to the next game. Non eye-rollers: this game is awesome! Give it a go. I know, I know. I could easy have made this number 7. In lieu of an apology, Victoria McRitchie explains her journey to this superb game. Then I found out about BoardGameGeek. I started working my way through the top 20, and now my shelves are creaking under the weight of the European behemoths like Terra Mystica, Agricola and Through the Ages.

Their artwork, mechanics and themes are quite astounding in comparison to the relatively mundane bestsellers like Cranium or The Game of Life. An Indian takeaway and a 5 player game of 7 Wonders for me is my ultimate night in, sheer bliss, especially when I win! Are you trying to punch my ticket?

All aboard! The game itself does not. Those foolish peoples of Atlantis, rightly punished by vengeful gods for their decadent ways. A really great game where the players try to escape the sinking island occasionally helping each other but mainly feeding each others explorers to sharks and sea monsters!

The smiler is Will, who won! Not to be confused with the admittedly awesome computer game of the same name, Civilisation won strong approval from some of our readers, jolo among them. Probably my favourite game at the minute. Dice-chucking worker placement fun, but as with all my favourite games, the thing I like the most is the interaction between the people, superfun times make superfriends.

Ive been a gamer since my teens and play regularly. My favourite board game is coincidentally my favourite Sci Fi series Firefly. Takes some time and a lot of space but great fun! Gregg Lewis-Qualls likes it so much he even sent in a video. Tell me if you've heard this one before: A Dwarf, a thief, a fighter and a priest walk into a dungeon Warhammer Quest is the quintessential 'Old School' dungeon-crawler. Assemble wacky, wannabe superheroes from two different halves and then face off against an equally absurd supervillain and their entourage to earn more fame than anyone else.

A discovery and trading game where players have to explore trade routes and customise their ships to best exploit them. Most goods are in demand somewhere except for the Humans' main export - rock videos.

From a few hours, to most of a day depending on the number of players. We bought this at Essen , and it was brilliant. Essentially a train robbery game, and pretty much made for turning into a drinking game. Lots of fun! It took me a few years to come across the game that I could deem as my 'Grail Game' but eventually it happened. The limited amount of copies in existence does a lot to hinder its reputation but for the few who own a copy, it is something that remains in their collection as a treasure.

It is refined, meticulous and mentally stimulating. The rules are simple but the play is nothing short of genius. For most it's a game that's a easily passed over but for me it's my white whale. Nowadays, recommendation systems are widely used in many different applications, such as movie providers, advertisements, or social networks. The main idea of recommendation systems is to provide personalized recommendations in an automated way.

That is, the recommendations must be adapted to the specific interests or needs of each user, and they must be provided by a computer program, with no human supervision. If you are already familiar with recommendation systems and machine learning, you can safely skip the remainder of this text and check out our paper for the technical details. Otherwise, continue reading for a high-level overview. To provide the boardgame recommendations, we focus on collaborative filtering.

In collaborative filtering, we have a set of items boardgames that have been rated by users. The main idea of collaborative filtering is to infer the preferences of each user based not only on the ratings provided by that user, but also on the ratings of all other users in the system. These preferences are then used to form the actual predictions.

Thus, collaborative filtering makes use of the ratings of all users to make predictions about a given user. In other words, it recommends items that other users with preferences similar to yours rated positively. This is why it is called collaborative.

Some of the games have been rated by some of the users, but there are many items that have not been rated; these are indicated with a question mark. If we are able to predict the missing entries, then we can recommend the items that the user is more likely to rate positively. Here is where the machine learning approach comes into play. We make use of a statistical method to learn features from the observed ratings, and then we use these features to make predictions about the missing entries.

Our approach makes use of a matrix factorization approach combined with exponential family embeddings. Our ArXiv paper will be available soon. Stay tuned! We downloaded the data from boardgamegeek.